Year: 2021

29 Jun 2021

How VCs can get the most out of co-investing alongside LPs

It has rarely been easier for people looking to invest. Nontraditional investors, which include anyone outside of traditional VC firms investing in venture capital deals, are increasingly making their presence felt in the investing community.

McKinsey found that the value of co-investment deals has more than doubled to $104 billion from 2012 to 2018. And by some counts, there are as many as 1,600 “nontraditional” investors helping to fund venture capital deals in 2021.

The primary motivator for nontraditional investors is seeking better returns, and investing alongside VC funds is a great way to achieve that. A recent Preqin study shows co-investing funds significantly outperform traditional funds.

Research shows that 80% of investors found their co-investments outperforming private equity fund investments, with 46% outperforming by a margin of more than 5%. Investors also benefit from a generally less expensive fee structure compared to traditional private equity or VC funds.

When evaluating deals, keep in mind that most companies are not going to be the next tech unicorn, so set realistic views on exits.

Co-investors can also profit by sharing the investment risk, which benefits all investors and builds loyalty and trust. And because this kind of investing requires a hands-on approach, investors get the chance to work closely with top sponsors — the general partners (GPs) — to foster deeper relationships and gain a better understanding of the GPs’ investment strategies and deal review processes. For new investors, building these relationships is essential for strengthening their own investment skills in the long run.

Why VCs love alternative investors

Alternative investors aren’t the only ones who benefit from co-investing, it’s also a boon for GPs. They gain a broader array of funding options by partnering with alternative investors, and they can leverage their own capital more effectively with prospective investments.

VCs have other benefits too: While co-investing LPs remain passive in the business, the VC can use that voting power to preserve investor rights and consolidate decision-making. It also allows them to put more money to work in any company while staying within diversification limits.

29 Jun 2021

How VCs can get the most out of co-investing alongside LPs

It has rarely been easier for people looking to invest. Nontraditional investors, which include anyone outside of traditional VC firms investing in venture capital deals, are increasingly making their presence felt in the investing community.

McKinsey found that the value of co-investment deals has more than doubled to $104 billion from 2012 to 2018. And by some counts, there are as many as 1,600 “nontraditional” investors helping to fund venture capital deals in 2021.

The primary motivator for nontraditional investors is seeking better returns, and investing alongside VC funds is a great way to achieve that. A recent Preqin study shows co-investing funds significantly outperform traditional funds.

Research shows that 80% of investors found their co-investments outperforming private equity fund investments, with 46% outperforming by a margin of more than 5%. Investors also benefit from a generally less expensive fee structure compared to traditional private equity or VC funds.

When evaluating deals, keep in mind that most companies are not going to be the next tech unicorn, so set realistic views on exits.

Co-investors can also profit by sharing the investment risk, which benefits all investors and builds loyalty and trust. And because this kind of investing requires a hands-on approach, investors get the chance to work closely with top sponsors — the general partners (GPs) — to foster deeper relationships and gain a better understanding of the GPs’ investment strategies and deal review processes. For new investors, building these relationships is essential for strengthening their own investment skills in the long run.

Why VCs love alternative investors

Alternative investors aren’t the only ones who benefit from co-investing, it’s also a boon for GPs. They gain a broader array of funding options by partnering with alternative investors, and they can leverage their own capital more effectively with prospective investments.

VCs have other benefits too: While co-investing LPs remain passive in the business, the VC can use that voting power to preserve investor rights and consolidate decision-making. It also allows them to put more money to work in any company while staying within diversification limits.

29 Jun 2021

The engineering daring that led to the first Chinese personal computer

China is one of the world’s wealthiest digital economies today, with a hardware supply chain that is unrivaled and a panoply of prominent and massively profitable companies like Alibaba, Tencent and ByteDance taking a leading role in the world. Yet, all of this cutting-edge innovation rests on a forty-year-old solution to one of the great computing challenges: the development of Chinese word processing.

Beginning in the early 1980s, China dramatically expanded its computing purchases from the United States and the West, importing just 600 foreign-built microcomputers in the year 1980, as compared to 130,000 in 1985. Companies in the United States, Japan, and Europe clamored to get in on this “buying binge,” as one observer called it.

There was a major problem, however, both for potential Chinese computer users and Western manufacturers: no Western-built personal computer, printer, monitor, operating system, program, or otherwise was capable of handling Chinese character input or output—not in the early and mid-1980s, anyway, and certainly not “out of the box.” Without some major overhauls, mass-manufactured personal computers were effectively useless for anyone wanting to operate in Chinese.

One of the most important reasons was the problem of memory—specifically the memory required for Chinese fonts. At the advent of Latin alphabetic computing, Western engineers and designers determined that a font for English could be built upon a 5-by-7 bitmap grid—requiring only 5 bytes of memory per symbol. Although far from aesthetically pleasing, this grid offered sufficient resolution to render the letters of the Latin alphabet legibly on a computer terminal or a paper printout. Storing the 95 printable characters of US ASCII required just 475 bytes of memory—a tiny fraction of, for example, the Apple II’s then 48K of motherboard memory. 

To achieve comparable, bare-minimum legibility for Chinese characters, the 5-by-7 grid was far too small. When designing a bitmap font for Chinese, engineers had no choice but to increase the size of the Latin alphabetic grid geometrically, from 5-by-7 pixels to upwards of 16-by-16 pixels or larger, or at least 32 bytes of memory per Chinese character. The total memory required to store just the bitmaps (in either simplified or traditional form, but not both, and with no accompanying metadata) would equal approximately 256K for the 8,000 most commonly used Chinese characters, or four times the total capacity of most off-the-shelf personal computers in the early 1980s. All this, even before accounting for the RAM requirements for the operating system and application software.

Draft bitmaps from the Sinotype III Chinese font, prepared prior to digitization. Courtesy of Louis Rosenblum Papers, Stanford University Special Collections.

Such is the context for one of the great engineering histories of modern computing, a tale of entrepreneurial daring and engineering ingenuity that provides a unique look into the global development of the digital revolution.

This is the first of two articles on TechCrunch in which I examine the Sinotype III, an experimental machine which was among the first personal computers to handle Chinese-language input and output. Built atop a store-bought Apple II—but outfitted with a custom-programmed word processor and operating system—Sinotype III served as a “proof of concept” which demonstrated how one could “translate” Western-manufactured computers into Chinese, and thereby open up a vast new marketplace.

In this first part, I will examine the profound technical challenges around computer memory, fonts, and operating systems faced by the creators of Sinotype III, and how they devised novel solutions to overcome them.

The chutzpah of a newly minted graduate who had no immediate job prospects”

Our story begins with the Graphic Arts Research Foundation (GARF)—the organization where, arguably, Chinese computing was born. The Ideographic Composing Machine, also known as the Sinotype, was invented in the late 1950s by MIT electrical engineer, Samuel Hawks Caldwell with GARF funding. Following his untimely death in 1960, the project came to a standstill. During the 1960s and 1970s, the Sinotype project was kept alive by a number of different parties, including the Itek Corporation, RCA, and finally, GARF once again.

Keyboard of Sinotype I, designed by Samuel Caldwell in the late 1950s. Courtesy of Louis Rosenblum Papers, Stanford University Special Collections.

Sinotype’s homecoming was thanks in large part to one man: Louis Rosenblum. Born in 1921 in New York City, he was yet another member of the MIT family, graduating in 1942 with an undergraduate degree in Applied Math. Studying under Harold Edgerton, the world-renowned professor of Electrical Engineering (and who shot the famous “milk drop coronet” photo in in the 1930s), Rosenblum took a job at Polaroid immediately following graduation, working with Edwin Land on a variety of projects, including the development of instant photography. In 1954, he moved to Photon—where he worked on photocomposition of non-Latin writing systems. Deeply familiar with the late Caldwell’s pioneering work on Sinotype, Rosenblum effectively adopted the project, and revived it when he joined GARF as a consultant in the mid-1970s.

Diagram showing configuration of Sinotype II system, running on a Nova 1200 CPU. Courtesy of Louis Rosenblum Papers, Stanford University Special Collections.

GARF continued to work on the Sinotype project well into the early 1980s, by which point it had developed an advisory board featuring a host of renowned scholars, as well as those with deep China experience. Harvard linguist Susumo Kuno came on board; as did Richard Solomon, known for his pivotal role in Richard Nixon’s visit to the PRC in 1972 and then head of the Social Science Department at the RAND Corporation.

As stellar as this brain trust was, however, GARF’s major breakthrough on the Sinotype project—the leap from a minicomputer-based system (Sinotype II) to one based on a microcomputer (Sinotype III)—was catalyzed by a college student whose only experience at GARF to date was a brief, two-week gig working on data management for the Sinotype II project in 1979. He was Bruce Rosenblum, Louis Rosenblum’s son.

Bruce Rosenblum using the Sinotype III system. Courtesy of Louis Rosenblum Papers, Stanford University Special Collections.

As an undergraduate at the University of Pennsylvania and an aspiring photojournalist, Bruce was balancing his time between coursework and his role as Photo Editor for the independent student-run newspaper Daily Pennsylvanian. The paper was remarkably advanced in terms of the equipment it ran, as well as the deep expertise of the students in charge.

By the fall of Bruce’s junior year, the paper’s existing typesetting equipment (two Compugraphic typesetters) were on their last legs and needed to be replaced. Along with three of his student colleagues at the paper, Bruce assisted in the process of researching potential replacements, eventually settling on a combined $125,000 contract with two companies: Mycro-Tek in Wichita, Kansas, and Compugraphic, in Wilmington, Massachusetts.

As for the Sinotype project—one that Bruce was well aware of, thanks to his father, but with which he had no involvement—a pivotal moment came in early May 1981. Bruce had just completed his final exams, and stopped by the offices of the paper. His colleague Eric Jacobs was there, hard at work on a TRS-80 Model II personal computer from Radio Shack. Jacobs was contemplating how this microcomputer might be used to run the newspaper’s business operations. Bruce observed for perhaps thirty minutes, before heading on with his day. 

Those thirty minutes stuck with him, however. “It was the first time I’d ever seen anyone work on a microcomputer,” Bruce recalled by email to me, “and those few minutes were the inspiration that triggered the whole Sinotype III project and eventually my career in computers.”

Later that same week, Bruce made a somewhat off-the-cuff remark in a phone call with his father. Referencing the immense cost of the Data General hardware GARF was then using to build Sinotype II, Bruce remarked that someone could probably program something equivalent or better on a microcomputer for a fraction of the cost—perhaps with as little as $10,000 worth of hardware, as compared to the more-than $100,000 price tag for the equipment GARF was currently funding.

His father was intrigued. Louis asked Bruce if he himself might be up to the task of programming such a machine. Bruce boasted no formal training in computer science, although he had worked intensively with computers in high school, and taught himself both PDP-8 assembly language and BASIC. “Sure,” he responded to his father’s query with “the chutzpah of a newly minted graduate who had no immediate job prospects.”

During his world tour, Bruce Rosenblum continued to work on the Sinotype III project, including on notepaper from New Delhi. Courtesy of Louis Rosenblum Papers, Stanford University Special Collections.

In June 1981, Bruce had a formal meeting in New York with Bill Garth, Prescott Low, and his father Louis, to present his Sinotype III proposal. Bruce dressed for the part, arriving in a three-piece suit. In Bruce’s formal proposal, he cited a total of $7,500 in hardware costs, with an additional $5,000 for programming fees. The plan promised a Chinese word processor, running on an Apple II, delivered in approximately four months’ time. If this worked, it would reduce the cost of such a machine by an order of magnitude.

Bruce got the job and went on to program Sinotype III from June to November 1981, balancing time between this and his full-time job as a tour guide for the National Park Service at Independence Hall in Philadelphia. During daytime breaks he would write out assembly code by hand, transcribing it at night. When Labor Day in 1981 came, and Bruce’s tour guide job ended, he dedicated two months straight to finishing the code, and delivered it to GARF.

Memory hacking 

The first problem that GARF and the Rosenblums faced was that of computer memory. Developers of early Chinese personal computers explored every available option in their effort to juice as much memory as possible out of their systems. We will explore two strategies in particular, sometimes employed in isolation, but often in concert: Adaptive Memory and Chinese Character Cards.

The Sinotype III system comprised five components: a Sanyo DM5012CM 12-inch monitor; an Epson MX-70 printer; a Corvus 10 MB “Rigid Disk Storage” for storing the Chinese character bitmap database and their corresponding “descriptor codes”; an Apple Disk Drive “for storage of text files”; and the Apple II itself.

Out of the box, the Apple II came with 32K of RAM, extensible to 48K on the motherboard. “We maxed that out even before the Apple II left the store,” Bruce Rosenblum remarked by email to me. 48K of memory was still far too little for his purposes, however, and so Bruce opted for what, at the time, was a fully standard modification, commonly employed by so-called “power users” of the era: namely, to insert an additional 16K memory card in Slot 0, thereby bringing the total available memory to 64K. 

Even this was too little, however. “I needed more RAM to store a full encoding system,” he said, “and also the 16-by-16 bitmaps for the 100 most frequent ideographs.”

He began to explore a “mod” of the Apple II that few if any others had tried before. “Somehow,” he said, “I figured out I could put a second 16k board in slot 2 of the Apple II, and that gave me a total of 80k.” “Completely non-standard,” he continued, “but it worked with off-the-shelf components.”

This modification pushed the machine past its own limitations, however. The 6502 microprocessor on the Apple II was only capable of accessing 64K of memory directly—meaning that, even with the additional 16K Bruce had managed to bootstrap in with the second memory board, there was simply no built-in way for the Apple II to simultaneously access these additional addresses in memory. So “non-standard” was this mod that, when he told an Apple engineer about it during one of his many conversations, the Apple rep was shocked—he had never heard of, or thought of, doing such a thing. 

To enable the Apple II to access 80K of memory, rather than just 64K, Bruce dispensed with the out-of-the-box operating system and programmed his own in assembly language. Key to his custom-designed program was the possibility of “selecting between two banks of 16K that overlap each other.” In other words, although only 64K worth of memory locations would be accessible at any one instant, by rapidly oscillating between the two memory expansion cards, he could in effect trick the computer into accessing both at speeds that, from the perspective of the user, would have been negligible. That squeezed 25 percent more memory out of the system, enabling the inclusion of perhaps as many as 400 more Chinese characters in on-board memory.

Bruce delivered the final code to GARF the week before Thanksgiving, and then set out on a world backpacking tour that would take him across Europe and Asia. From this point on, development of Sinotype III would be largely in the hands of Louis Rosenblum and GARF, although Bruce continued to serve as a consultant, exchanging frequent correspondence with his father from wherever in Europe, China, India, or elsewhere he found himself at the moment.

Speeding toward real-time Chinese typing

Even with his ingenious mod, however, Louis and Bruce estimated that a mere 600 to 1000 Chinese characters would be able to fit in on-board memory. When accounting for the size of Sinotype III’s operating system, program applications, and the memory requirements of each Chinese character, the vast majority of Chinese characters in the machine’s lexicon would need to be stored somewhere else, whether on floppy disks, an external hard drive, or via some other hardware solution. 

Sinotype III Computer Monitor. Courtesy of Louis Rosenblum Papers, Stanford University Special Collections.

Early on, Bruce briefly contemplated using PROM (Programmable Read-Only Memory) chips—but this idea quickly revealed itself to be a dead end. Circa 1981 and 1982, the largest PROM chips on the market maxed out at 2K of memory, which translated into a mere 28 to 51 Chinese characters. In order to store 7,000 Chinese characters in this fashion, then, Bruce would have needed either 138 or 250 PROM chips. “That’s a lot of chips,” he remarked.

Bruce then considered the possibility of storing characters on floppy disks. This, too, proved unworkable, not only because of the large number of disks it would have required, but also the slow access and retrieval speeds involved in fetching character bitmaps from floppy drive storage. GARF opted instead for a third solution: to outfit Sinotype III with an external hard drive, which at the time was an almost unheard-of microcomputer accessory. In order to overcome the profound memory limitations, GARF would store thousands of lower-frequency Chinese characters “off-site” in the system’s external hard drive: a 10 MB Corvus “Rigid Disk Storage.”

This had negative implications for the operating speed of Sinotype III, however. Within the space-time continuum of computing, in which most operations take place at blazing sub-second speeds, hard drives were cumbersome beasts. Particularly at this time, they relied on rigid magnetic disks—“platters”—that rotated within the device, not unlike a record player. The contents of various “tracks” were read by a head, similar to how the grooves on a record are read by the needle. Retrieval speeds depended upon the location of the head, and the particular rotational position of the disk at the moment of the retrieval request. Not unlike arriving at the stop to find that the bus has just departed, one had no option except to wait until the bus came back around again.

In concrete terms, retrieval times for Chinese characters stored on the hard drive were 10 times slower than those stored in RAM. Specifically, the retrieval time for those Chinese characters stored in RAM could be achieved in approximately 100 milliseconds per character—a unit of time imperceptible by human cognition. As for the characters stored in external storage, however, the input of any of these characters required as much as a full second to access and retrieve—a unit of time well within the threshold of human perception.

A one-second input time would have proven devastatingly slow within the context of mid-1980s personal computing, where users in English-language contexts were quickly becoming accustomed to real-time typing. In addition, one second is, obviously, ten times as long as 100 milliseconds, meaning that the average user would be able to feel this differential each and every time he or she wished to input lower-frequency characters.

In order to mitigate this problem, Louis Rosenblum hit upon an idea which he referred to as “adaptive temporary storage.” Sinotype III would be able to adjust the set of characters stored in RAM depending upon what the user had recently inputted. Upon initial boot, Sinotype III’s on-board RAM would be outfitted only with a predetermined set of high-frequency characters. The inputting of any hard-drive-based infrequent character would take up to one second, as noted above. However, “as each of the less frequent ideographs is keyboarded,” he explained in a letter at the time, “its code and dot matrix pattern will be noted in the random access memory.” In other words, such characters would be temporarily copied from the hard drive to on-board RAM cache, thereby reducing subsequent retrieval times.

Internal GARF document showing Sinotype III character database and metadata. Courtesy of Louis Rosenblum Papers, Stanford University Special Collections.

Chinese-on-a-Chip

Even with recourse to toggling and adaptive memory, there remained many thousands of characters that fell beyond the limits of such strategies. While high-frequency Chinese characters accounted for a large percentage of overall usage, the production of any kind of technical or specialist content would have certainly brought the user repeatedly into the “off-site” repository of Chinese characters. More of these “low-frequency” characters needed to be brought “on-site” if the experience of Chinese computing was ever going to approach the same feeling of instantaneity enjoyed by English-language counterparts. 

Engineers in the late 1970s and early 1980s began to explore a different hardware solution, referred to as “Chinese Character Cards” (Hanka), “Chinese Cards” (Zhongwenka), “Chinese Character Generators,” “Chinese Font Generators” (Hanzi zimo fashengqi) or, as one article delightfully referred to them, “Chinese-on-a-Chip.” Much like memory cards and graphic cards, “Chinese character cards” were designed to be installed directly into motherboard expansion slots. Hardwired into these cards were thousands of Chinese bitmaps and input encodings. In effect, they served the same role as an external hard drive, but at far faster speeds and with more reliable performance. 

“Chinese-on-a-chip” cards were not the focus of research at GARF. Rather, they grew out of the earlier era of custom-designed Chinese systems, all prior to the personal computing revolution. These included systems such as the Ideographix IPX, by Chan Yeh, and the Olympia 1011, which were outfitted with microprocessors whose sole purpose was the generation of character bitmaps and the storage of input descriptors. On the Olympia 1011 Chinese word processor—basically a single-purpose electric Chinese typewriter—one of the three Intel 8085 processors was dedicated exclusively to Chinese character generation.

During the early 1980s, such character generators were commoditized and turned into saleable products themselves. No longer did one need to buy a full-fledged word processor, such as the Olympia 1011, to gain access to this kind of on-board character generator. Instead, one could purchase a “Chinese Character Card” and then install it on one’s personal computer of choice. 

Among the earliest centers of Chinese computing to focus on Chinese Character Cards was Tsinghua University, where researchers developed an early card capable of storing approximately 6,000 Chinese bitmap patterns in 32-by-32 dot matrix format. By the mid- and late-1980s, there were dozens of different “Hanka” on the market, manufactured and marketed by companies across Japan, China, Taiwan, Hong Kong, the United States and elsewhere.

By the mid- and late-1980s, the “Chinese-on-a-chip” approach became so important and common that practically all computers boasting Chinese or Japanese-language capabilities featured a character generator card of one sort or another.

Thus, from the 1950s with Caldwell’s Sinotype to the duo father-son Rosenblum team and GARF around Sinotype III in the 1980s, solving the memory problems associated with Chinese characters was the linchpin to opening the Chinese market to computing. Hacking computers with more memory, creating adaptive memory algorithms for prioritizing characters, and building dedicated hardware bridged the problem and initiated the computer revolution in China.

Yet, the next step was how to expand beyond the computer itself to everything that might connect to it. In part two of this series, coming up shortly on TechCrunch, our discussion will continue with a deep-dive into the challenges of designing and programming early computer monitors, printers, and other peripherals capable of handling Chinese text output. 

29 Jun 2021

Zomato’s $100 million investment to turn Grofers into a unicorn

Indian food delivery giant Zomato, which is working to explore the public markets later this year, has reached an agreement to invest $100 million in online grocer Grofers for about 10% stake in the seven-year-old startup, according to a source and multiple others familiar with the matter.

The proposed investment values Grofers, which counts SoftBank as its largest investor, at over $1 billion. (Indian regulator, the Competition Commission of India, needs to approve the investment.) Zomato’s proposed investment is part of a broader round, in which others including Tiger Global and SoftBank Vision Fund 2 are expected to chip in some capital. Zomato said it had no comment.

The leadership teams at Grofers and Zomato have long been close friends and began exploring this investment earlier this year. Both the firms are also open to the idea of Zomato acquiring a majority stake in Grofers in the coming quarters, though a decision hasn’t been reached and won’t be fully explored until Zomato becomes a publicly traded company, the source told TechCrunch.

Zomato, which acquired Uber’s Indian food delivery business early last year, has told some of its major investors that it envisions a future where the Gurgaon-based firm has expanded much beyond the food delivery category, the source said, requesting anonymity as the talks are private.

Grofers operates an online grocery delivery service in India. The startup has witnessed a sharp surge in its popularity in the past year as several Indian states enforced strict lockdown restrictions to contain the spread of the virus. The startup competes with BigBasket, which recently sold its majority stake to Indian conglomerate Tata Group.

The Indian online grocery market has seen a new player emerge in the past one year, Reliance Industries, India’s most valuable firm. Reliance, which operates the largest retail chain in India, last year launched JioMart.

“JioMart’s growth is a testament to its already loyal customer base, 80% of whom are repeat shoppers. JioMart New commerce’s aim is to transform and grow the small merchant ecosystem, so our merchant partners prosper. Over the past year, over 300,000 merchant or shopkeeper partners across 150 cities were enabled and empowered to transform their businesses both physically and digitally,” said Mukesh Ambani, the chairman of Reliance Industries, earlier this month.

In a note to clients earlier this year, Bank of America analysts estimated that the online grocery delivery market could be worth $12 billion in India by 2023.

“Competition is high in the sector with large verticals like BigBasket/Grofers and horizontal like Amazon/Flipkart trying to convert the unorganized market to organized one. Till recently the No 1 player in the space was BigBasket, with it hitting $1 billion annualized GMV & selling over 300,000 orders every day. Reliance Industries also threw its hat with the company launching its JioMart app in May-20 across 200 cites,” they wrote.

29 Jun 2021

Family app Life360 announces $2.1M investment round from celebs and influencers

Family communication and tracking app Life360 has announced a new investment round that will see the company bringing on board a number of “celeb” investors and influencers who, combined, will form a new “Family Advisory Council” to help shape Life360’s future product direction and marketing. The round, which is approximately $2.1 million in size, was led by Bryant Stibel, the firm co-founded by the late Kobe Bryant and business partner Jeff Stibel. Others in the round included Vanessa Bryant, Joanna and Chip Gaines, Tony Hawk, Chris and Jada Paul, TikTok influencer Billy Perry, and Nicole and Michael Phelps.

Life360 has traded on the Australian Securities Exchange (ASX) since listing two years ago, so this round is more about bringing on new stakeholders who can also help attract more attention to Life360’s service. The company says it’s currently on track to top $110 million USD in revenue this year for its app now used by over 28 million monthly users across 195+ countries. As of March 2021, 916,000 families are paying for Life360’s service.

The celeb investors along with Life360 will form the Family Advisory Council which will draw on the advisors’ own family experiences to help inform feature developments and shape the future of the product and marketing strategy, Life360 says.

The company has been working to be more responsive to family members’ concerns, as it wants to position its app as something all family members want to use — not just helicopter parents snooping on their kids. In fact, Life360 CEO Chris Hulls took to TikTok last year to listen to teens’ complaints about their lack of privacy, then used that to develop a more privacy-respecting feature called “Bubbles.” The feature shows a bubble around a general location, not a blue dot with an exact location. This is meant to give teens a sense of the freedom they crave, while also helping parents and kids establish better trust.

The new Family Advisory Council could help Life360 streamline similar sorts of input from families, it appears.

“Investing and advising in companies is typically an adult thing, not something you do with your children,” said Hulls, in a statement about the investment. “We’re creating a unique opportunity to advise on a product side by side with your kids. Having the support of these icons speaks volumes to our long term vision to be the leading provider in family safety services. Life360 wants to create a brand that feels meaningful and relevant for both parents and kids. So it’s only natural that we would ask our investors to participate in the same spirit,” he added.

“One of my passions is ensuring children get the opportunities they deserve,” noted new investor, Vanessa Bryant (Kobe’s widow). “Life360 helps families feel safe and protected by making carpooling, pickup and drop-offs easier for parents, while also providing locations at their kids’ schools, activities and sports practices. Having modern tools like driving information, speed and phone usage makes me feel a lot more at ease, especially with my teenage driver. I love the fact that I can see my daughter’s location and speed in a vehicle whether she’s driving or as a passenger,” her statement said.

Though best known for its location services, Life360 has been working to establish itself as more than just a family tracker, given the competition from apps like Find My that now come built into mobile devices, as well as services provided by mobile operators. Today, Life360’s suite of family tools includes those for driving safety, emergency assistance, identity protection, and more.

Earlier this year, Life360 also announced the acquisition of wearable device maker Jiobit to expand its tracking abilities to include family members without phones, like young children and even pet.

That $37 million deal will close in about 30 days, the company tells us.

The addition of the new investors follows Life360’s appointment of Randi Zuckerberg to its Board of Directors earlier this year, and last year’s addition of new C-Suite execs, CFO Russell Burke and CPO Jonathan Benassaya, to focus on the company’s business mode and product offerings, respectively.

29 Jun 2021

Shopify drops its App Store commissions to 0% on developers’ first million in revenue

Following similar moves by Apple, Google, and more recently Amazon, among others, e-commerce platform Shopify announced today it’s also lowering its cut of developer revenue across its app marketplace, the Shopify App Store, as well as the new Shopify Theme Store. The news was announced today alongside a host of other developer-related news and updates for the Shopify platform at the company’s Unite 2021 Conference, including updates to Checkout, APIs, developer tooling and frameworks, among other things.

Shopify says its app developer partners earned $233 million in 2020 alone, more than 2018 and 2019 combined — an increase that can likely be attributed, in part, to the COVID-19 pandemic and the rapid shift to e-commerce that resulted. Today, there are over 6,000 publicly available apps across the Shopify App Store, and on average, a merchant will use around six apps to run their business.

Now, Shopify says it will drop its commissions on app developer revenue to 0%, down from 20%, for developers who make less than $1 million annually on its platform. This benchmark will also reset annually, giving developers — and, particularly those on the cusp of $1 million — more earning potential. And when Shopify’s revenue share kicks in, it will now only be 15% of “marginal” revenue. That means developers will pay 15% only on revenue they make that’s over the $1 million mark.

The same business model will apply to Shopify’s Theme Store, which opens to developer submissions July 15.

As the two stores are separate entities, the $1 million revenue share metric applies to each store individually. The new business model will begin on August 1, 2021 and will be made available to developers who register by providing their account details in their partner dashboard.

Shopify says the more developer-friendly business model will mean a drop in company revenue, but says it doesn’t expect this impact “to be material” because it will encourage greater innovation and development.

The changes to Shopify’s App Store follow a shift in the broader app store market around developer commissions.

Last year, amid increased regulatory scrutiny over how it runs its App Store, Apple announced it would reduce the App Store commissions for smaller businesses under a new program where developers earning up to $1 million per year would only have to pay a 15% commission on in-app purchases. Google and Amazon have since followed suit, each with their own particular spin on the concept. For example, in Google’s case, the fee is 15% on the first million the developer earns. Amazon is still charging a higher percentage at 20%, but is tacking on AWS credits as a perk.

Apple and Google, in particular, hope these changes can help shield them from antitrust investigations over their alleged app store monopolies, while also giving developers a better reason to participate in their own slice of the app economy.

Outside of mobile, Microsoft this year agreed to match the 12% cut on game sales that Epic Games takes on its Windows Store, as a means of increasing the pressure on its rivals. With the larger update to the new Windows 11 Store, it will allow developers to use their own payment platforms, while keeping its commission at 15% on apps.

To date, much of the momentum in the market has been focused on lowering the cut of app and games sales. Shopify’s app platform is different — it’s about apps that are used to enhance an e-commerce business, like those that help with shipping and delivery, marketing, merchandising, store design, customer service and more. These are not consumer-facing apps, but they are still marketed in an app store environment.

While the changes to developers’ businesses is the big news today from Unite 2021, that’s not to diminish from the host of updates Shopify announced related to its larger platform.

Among the updates are: the debut of Online Store 2.0, a more flexible and customizable update to Shopify’s Liquid platform (its templating language), which Netflix was the first to test; investments in custom storefronts for faster response times; a new React framework for building custom storefronts called Hydrogen; a way to host Hydrogen storefronts on Shopify called Oxygen; support for more Metafields for products and product variants and custom content that’s built on top; speedier Spotify Checkout; Checkout Extensions (customizations built by developers); easier and more powerful Shopify Scripts; a Payments Platform for integrating third-party payment gateways into Checkout; updates to its Storefront API; and more.

The company today also shared a few more business metrics, noting, for instance, that last year over 450 million people checked out on Shopify, totaling $120 billion in gross merchandise volume. It said its Shopify partners — which include app developers, theme builders, designers, agencies and experts — earned $12.5 billion in revenue in 2020, up 84% year-over-year, and 4x the revenue of Shopify’s own platform.

29 Jun 2021

Nansen raises $12M from a16z to help investors make sense of crypto markets

While the ambitions of crypto investors have swelled even faster than the market has in recent months, institutional players have had a mountain of blockchain data to try to make sense of without particularly mature analytics products at their disposal.

Blockchain analytics startup Nansen is building a product for crypto traders and hedge funds to more confidently navigate the world of decentralized finance. Their product analyzes public blockchain information across some 90 million Ethereum wallets to clue users into evolving opportunities.

“Nansen’s high quality data enables investors to follow where the smart money is moving, where influential investors are taking positions as well as for discovering new projects to invest and perform due diligence,” Nansen CEO Alex Svanevik tells TechCrunch in an email.

The startup just closed a $12 million Series A led by Andreessen Horowitz (a16z), which recently unveiled a whopping $2.2 billion crypto fund designed to bankroll the firm’s crypto land grab. Other investors in the round include Coinbase Ventures, Skyfall Ventures, imToken Ventures, Mechanism Capital and QCP Capital.

Nansen’s primary product is a network of dashboards designed around specific verticals in the crypto space.

Beyond the very hot DeFi space, Nansen is tapping their labeled database to find investor opportunities in yield farming, liquidity pools, DEX data, and even helping traders scout out particular hot NFT collections. One of its popular dashboards called “Token God Mode” allows investors to tap into blockchain data on a particular ERC20 token, witnessing movement across exchanges over time as well as notable transactions across individual wallets.

Image via Nansen

As the crypto industry largely aims to bring more retail investors into its fold, Nansen’s pricing showcases an effort to bring in a fairly wide range of customers. The startup sells a $116 per month package designed to help traders tap into real-time analytics across a variety of market indicators, while also shopping a $2,500 per month plan designed to foster a closer relationship with more bespoke support access including weekly calls, exclusive chat groups and vertical-specific information sessions.

The team has been publishing some of its higher-level data publicly on its site, but saves the more granular up-to-the-moment data for its network of paying customers. Some of Nansen’s customers include crypto-centric funds like Polychain, Three Arrows, Pantera, and Defiance Capital.

29 Jun 2021

5 policies Washington should enact to end the climate crisis and joblessness

The importance of the U.S. startup ecosystem was made crystal clear during the pandemic: Many of us came to rely on new technologies that had been developed over the past decade, including revolutionary vaccines and testing devices, cutting-edge video-conferencing software that kept workers productive and kids learning online, and financial technology that allowed restaurants and other small businesses to move their operations online to survive.

As we move into this period of national recovery, high-tech startup companies — and the venture-capital investors who back them — are poised to play a critical role in creating higher-paying jobs across the country. These jobs can be created in both the traditional U.S. technology centers and regions hit hard by the decline in manufacturing. Venture investors also help create and deploy technology (think advanced computer chips and electric-car batteries) that increase America’s economic competitiveness vis-à-vis China and help address the climate crisis. All of these are key goals of the Biden administration’s ambitious new jobs plan.

But these worthy goals could be hamstrung by policies that fail to account for the unique business model of high-tech startups. We must realize that we are in an increasingly fierce global competition for innovation. The share of global venture-capital dollars going to U.S. companies has dropped precipitously over the last two decades, from 84% in 2004 to 51% last year. Given that venture capital plays an enormous role in creating economic value, including new jobs, innovation, economic growth and tax revenues, we must refocus our efforts to keep the U.S. positioned as a global innovation and research and development leader.

Here are five policy recommendations we encourage Washington to consider:

Make it easier for brilliant entrepreneurs from other countries to start companies in the U.S. There is a guaranteed way to create new American companies: Pass a startup visa that recruits the world’s most talented entrepreneurs to our shores. Immigrant entrepreneurs have created thousands of U.S. companies, including Zoom, Intel and Moderna. But our immigration policy pushes away foreign-born founders because the U.S. does not have a dedicated visa category for job creators, while more than 20 other countries now have a startup visa category.

There is a guaranteed way to create new American companies: Pass a startup visa that recruits the world’s most talented entrepreneurs to our shores.

Enact policies like the Endless Frontier Act to grow economies in all regions and communities. The United States is the global leader in science and technological ingenuity and innovation. To maintain this leadership at a time when new technological capabilities are being adopted across all elements of our society, we must prioritize technology-focused economic development and create the jobs of the future here.

The key legislative proposal that the president’s plan relies on to achieve this is the Endless Frontier Act. This bipartisan bill, which is now moving through Congress, proposes a generational investment in federal basic research and technology commercialization activities that would lead to new high-tech companies being formed across the country, more technologies designed to address critical societal challenges, increased domestic manufacturing capacity, and greater economic opportunities for workers and communities. The Endless Frontier Act rightfully prioritizes new company formation and growth to encourage the participation of venture capital investors and entrepreneurs who will ultimately create and scale new American companies.

Utilize the innovative power of startups to address the climate crisis. Global carbon emissions are driving a rapidly increasing environmental crisis that will be one of the greatest challenges for our generation to solve. Fortunately, there are thousands of American entrepreneurs at work today building technologies to address the crisis, including new energy sources and storage, clean transportation technologies, carbon capture and utilization, and new, environmentally focused agricultural technologies. The president’s bold plan must take advantage of this generation of innovative startups because their success will be a major factor in the rate of our progress, and we know this is a race we can’t afford to lose.

Coordinate workforce development programs with new job creation opportunities at emerging companies. The jobs of the future are being created every day at VC-backed startups and emerging companies. As Congress considers how to craft workforce development programs, they should consider those that provide on-ramps to workers for jobs in the next generation of American companies, such as offering a refundable tax credit for emerging companies that create training programs for prospective employees. This could prove particularly effective at training non-college-educated workers for positions at high-growth companies.

Coordinate tax policy with the administration’s jobs strategies. We want to be constructive partners in these worthy efforts to expand economic opportunity and address societal challenges. But we caution that the administration’s proposals to increase taxes on capital gains, including carried interest, by more than 80% undercut our goals by specifically targeting the very entrepreneurs and long-term investment funds whose participation will ultimately determine whether the Build Back Better agenda is successful. We urge the administration to give the jobs plan every chance to succeed and avoid creating unintentional bottlenecks in the technology commercialization process with unprecedented tax increases.

As our economy continues to recover from the pandemic and we address the societal challenges of access to economic opportunity, climate and U.S. global competitiveness, we must remember that our country boasts the most vibrant startup ecosystem in the world. This ecosystem has provided technology to help us weather the pandemic and hopefully bring it to a close; launched the internet, biotechnology and climate technology industries; and led to the creation of millions of high-paying jobs.

The problems we seek to address may be unique to our time, but the source of our solutions remains the same. Expanding entrepreneurial activity will identify and scale the technologies needed to move our country forward and provide for a more secure and prosperous future for all. Let’s focus on working together to leverage this strength to solve our long-term challenges.

29 Jun 2021

Duolingo’s S-1 depicts heady growth, monetization, new focus on English certification

Duolingo filed to go public yesterday, giving the world a deep look inside its business results and how the pandemic impacted the edtech unicorn’s performance. TechCrunch’s initial read of the company’s filing was generally positive, noting that its growth was impressive and its losses modest; Duolingo recently began making money on an adjusted basis.

While the company’s top-level numbers are impressive, we want to go one level deeper to grow our understanding of the company beyond our EC-1.

Duolingo is likely entering a period in which it will have to invest heavily in features like pronunciation, efficacy and new apps — which could come at a steep upfront cost.

First, we’ll explore the growth of Duolingo’s total user base, how much money it makes per active user, and how effectively the company has managed to convert free users to paid products over time. The numbers will set us up to understand what else can be learned about Duolingo’s business beyond our original deep dive into the company’s finances — specifically underscoring the pressure cooker it finds itself in when looking for new revenue sources.

Starting with Duolingo’s growth in total active users, guess how fast they rose from 2019 to 2020. Hold that number in your head.

The actual numbers are as follows: In 2019, Duolingo closed the year with 27.3 million monthly active users (MAUs); it wrapped 2020 with 36.7 million MAUs. That’s a gain of 34%. If we narrow our gaze to Q1 2021 numbers compared to Q1 2020, we can see that Duolingo’s MAUs rose from 33.5 million to 39.9 million, or growth of around 19%.

The bulk of Duolingo’s growth, then, came in early 2020 when we consider its pandemic bump. Put more simply, the company scaled from 27.3 million MAUs at the end of 2019 to 33.5 million MAUs at the end of Q1 2020; from then, the company added 3.2 million more MAUs throughout 2020 and 6.4 million during the next four quarters.

Another lens through which to view the numbers is simply a recognition that first-quarter results at Duolingo appear to be stronger than results in the rest of the year, perhaps due to New Year’s resolutions to learn a new language or brush up on a second language learned in high school.

Next, let’s examine Duolingo’s monetization efforts regarding converting free users to paying users.

Here we can see a very different growth story. While the company’s MAUs rose 34% from 2019 to 2020, the company’s paying users rose from 900,000 at the end of 2019 to 1.6 million at the end of 2020. That is a far sharper gain of 84% on a year-over-year basis.

So, while Duolingo did see material user growth during 2020, it saw turbocharged expansion in the users it was able to shake revenue from. Improved monetization, more than acceleration in user growth, was the pandemic’s effect on Duolingo.

What can we see in the company’s more recent results? From Q1 2020 to Q1 2021, Duolingo’s paid subscribers rose from 1.1 million to 1.8 million, a gain of around 64%. That was a slower pace than the company managed more generally in 2020, which matches Duolingo’s slower revenue growth in Q1 2021 than it recorded in 2020.

The number is still strong, we think. But not as impressive as the more than 100% revenue expansion that the company put on the board last year.

In percentage terms, 3.3% of Duolingo’s MAUs were paid subscribers in 2019. That figure rose to 4.4% in 2020. And in Q1 2021, it reached 4.5%. Duolingo rounds that number to 5% in its S-1, which feels somewhat aggressive to us, given the somewhat modest pace at which the metric is improving. Here’s the wording:

As of March 31, 2021, approximately 5% of our monthly active users were paid subscribers of Duolingo Plus. Our paid subscriber penetration has increased steadily since we launched Duolingo Plus in 2017 and, combined with our user growth, has led to our revenue more than doubling every year since.

A gain of 0.1 percentage point in a quarter is growth, we suppose.

Next, let’s chat about revenue per MAU. To get consistent numbers, we’ll divide quarterly revenues by MAU figures from the same period. So, we’ll compare Q4 2019 revenue at Duolingo with its year-end MAU figure. We’ll do the same for 2020, and for Q1 2021 we’ll use both numbers from that period.

29 Jun 2021

Sources: SentinelOne expects to raise over $1B in NYSE IPO tomorrow, listing with a $10B market cap

After launching its IPO last week with an expected listing price range of $26 to $29 per share, cybersecurity company SentinelOne is going tomorrow with some momentum behind it. Sources close to the  tell us that the company, which will be trading under the ticker “S” on the New York Stock Exchange, is expecting to raise over $1 billion in its IPO, putting its valuation at around $10 billion.

Last week, when the company first announced the IPO, it was projected that it would raise $928 million at the top end of its range, giving SentinelOne a valuation of around $7 billion. Coming in at a $10 billion market capitalization would make SentinelOne the most valuable cybersecurity IPO to date.

A source said that the road show has been stronger than anticipated, in part because of the strength of one of its competitors, CrowdStrike, which is publicly traded and currently sitting at a market cap of $58 billion.

The other reason for the response is a slightly grimmer one: cybersecurity continues to be a major issue for businesses of all sizes, public organizations, governments and individuals. “No one wants to see another SolarWinds, and there is no reason that there shouldn’t be more than one or two strong players,” a source said.

As is the bigger trend in cybersecurity, Israel-hatched, Mountain View-based SentinelOne‘s approach to combat that is artificial intelligence — and in its case specifically, a machine learning-based solution that it sells under the brand Singularity that focuses on endpoint security, working across the entire edge of the network to monitor and secure laptops, phones, containerised applications and the many other devices and services connected to a network.

Last year, endpoint security solutions were estimated to be around an $8 billion market, and analysts project that it could be worth as much as $18.4 billion by 2024 — another reason why SentinelOne may have moved up the timetable on its IPO (last year the company’s CEO Tomer Weingarten had told me he thought the company had one or two years left as a private company before considering an IPO, a timeline it clearly decided was worth speeding up).

SentinelOne raised $267 million on a $3.1 billion valuation led by Tiger Global as recently as last November, but it has been expanding rapidly. Growth last quarter was 116% compared to the same period a year before, and it now has more than 4,700 customers and annual recurring revenue of $161 million, according to its S-1 filing. It is also still not profitable, posting a net loss of $64 million in the last quarter.