Monthly Archives: August 2021

News: Extra Crunch roundup: Corp dev handbook, Chicago startups, Brazil’s e-commerce landscape

• August 20, 2021
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If you’re a founder who finds yourself in a meeting with a VC, try to remember two things — you’re the smartest person in the room, and investors are looking for a reason to say “yes.”

If you’re a founder who finds yourself in a meeting with a VC, try to remember two things:

1. You’re the smartest person in the room.
2. Investors are looking for a reason to say “yes.”

Even so, many entrepreneurs squander this opportunity, often because they direct questions or fail to understand their BATNA (best alternative to a negotiated agreement).

“As the venture landscape becomes more a meritocratic environment where resumes and institutional affiliations matter less, these strategies can make the difference between a successful fundraise and a fruitless meeting,” says Agya Ventures co-founder Kunal Lunawat.

Whether you’re already in the fundraising process or plan to be in the future, be sure to read “A crash course on corporate development” that Venrock VP Todd Graham shared with us this week.

“If you’re going to get acquired, chances are you’re going to spend a lot of time with corporate development teams,” says Graham. “With a hot stock market, mountains of cash and cheap debt floating around, the environment for acquisitions is extremely rich.”

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On Wednesday, August 24 at 3 p.m. PDT/6 p.m. EDT/11 p.m GMT, Managing Editor Danny Crichton will host a conversation on Twitter Spaces with Eric Dean Wilson, author of “After Cooling: On Freon, Global Warming, and the Terrible Cost of Comfort.”

Wilson’s book explores the history of freon, a common refrigerant that was later banned due to its devastating impact on the ozone layer. After their discussion, they’ll take questions from the audience.

Thanks very much for reading Extra Crunch this week! I hope you have an excellent weekend.

Walter Thompson
Senior Editor, TechCrunch
@yourprotagonist

Apple is changing Mail Privacy Protection and email marketers must prepare

Apple iPhone, Apple Mail and Apple iPad account for nearly half of all email opens, but the privacy features included with iOS 15 will allow consumers to block marketers from seeing their physical location, IP address and tracking data like invisible pixels.

Email marketers rely heavily on these and other metrics, which means they should prepare now for the changes to come, advises Litmus CMO Melissa Sargeant.

In a detailed post, she shares several action items that will help marketing teams leverage their email analytics so they can “continue delivering personalized experiences consumers crave.”

Let’s make a deal: A crash course on corporate development

Venrock Vice President Todd Graham has some frank advice for founders at venture-backed startups: “It would be wise to generate a return at some point.”

With that in mind, he authored a primer on corporate development that lays out the three most common categories of acquisitions, tips for dealing with bankers, and explains why striking a partnership with a big company isn’t always the best way forward.

Regardless of the path you choose, “you need to take the meeting,” advises Graham.

“In the worst-case scenario, you’ll get a few new LinkedIn connections and you’re now a known quantity. The best-case scenario will be a second meeting.”

When VCs turned to Zoom, Chicago startups were ready for their close-up

The pandemic failed to slow the momentum of venture capitalists pouring money into startups, but Chicago stands out as an “outlying benefactor of accelerating venture capital activity and the rise of remote investing,” Alex Wilhelm and Anna Heim write for The Exchange.

When the world shut down and it didn’t matter if you were in NYC or SF (because everyone was on Zoom), the Windy City was ready to present itself as the venture champion of the Midwest.

What does Brazil’s new receivables regulation mean for fintechs?

The Brazilian Central Bank made a major reform to the way payments are processed that may throw the doors open for e-commerce in South America’s largest market.

Historically, merchants who accepted credit card payments had two options: Receive the full payment distributed over two to 12 installments, or offer a deep discount to receive a smaller sum up front.

But in June 2021, the BCB created new “registration entities” that permit “any interested receivables buyer/acquirer to make an offer for those receivables, forcing buyers to become more competitive in their discount offers,” says Leonardo Lanna, head of payment products at Monkey Exchange.

The new framework benefits consumers and sellers, but for the region’s startups, “it opens the door to a plethora of opportunities and new business models, from payments to credit.”

As its startup market accelerates, Brazil could be in for an IPO bonanza

An inflow of VC dollars, notable acquisitions and rising unicorn counts are all features of the Brazilian tech startup market, Anna Heim and Alex Wilhelm note in The Exchange.

“The IPO market in Brazil is changing,” they write. “TechCrunch noted last year that in the decade leading up to 2020, just two of the 56 IPOs in Brazil were technology companies. More recently, the number of technology companies listed in the country has swelled to at least 16, up from just four in 2019.”

Insider hacks to streamline your SOC 3 certification application

“For good reason, security certifications like the SOC 3 really put you through the wringer,” Waydev CEO Alex Cercei writes in a guest column.

Waydev, a Git analytics tool that helps engineering leaders measure team performance automatically, just attained the SOC 3 certification.

“We learned so much from the process, we felt it was right to share our experience with others that might be daunted by the prospect,” Cercei writes.

“So here’s our advice on how teams can smoothly reach an SOC 3 while simultaneously balancing workloads and minimizing disruption to users.”

Dear Sophie: Tips on EB-1A and EB-2 NIW?

Dear Sophie,

I’m on an H-1B living and working in the U.S. I want to apply for a green card on my own. I’m concerned about only relying on my current employer and I want to be able to easily change jobs or create a startup. I’ve been looking at the EB-1A and EB-2 NIW.

I’m not sure if I would qualify for an EB-1A, but since I was born in India, I face a much longer wait for an EB-2 NIW.

Any tips on how to proceed?

— Inventive from India

How to establish a health tech startup advisory board

Most startups could use an advisory board, but in health tech, it’s a core requirement.

Founders seeking to innovate in this area have a unique need for mentors who have experience navigating regulations, raising capital and managing R&D, to name just a few areas.

Based on his own experience, Patrick Frank, co-founder and COO of PatientPartner, shared some very specific ideas about who to recruit, where to find them and how to fit them into your cap table.

“You want to leverage these individuals so you are able to focus on the full view of the company to ensure it is something that both the market and investors want at scale,” says Frank.

Crypto world shows signs of being rather bullish

There’s no shortage of tech news to analyze, Alex Wilhelm notes, but this week, he took a fresh look at crypto.

How come?

“Because there are some rather bullish trends that indicate the world of blockchain is maturing and creating a raft of winning players,” he writes.

4 common mistakes startups make when setting pay for hybrid workers

In one recent survey, 58% of workers said they plan to quit if they’re not allowed to work remotely.

Startups that don’t offer employees work-from-home flexibility are at a competitive disadvantage, but figuring out how to pay hybrid workers raises a complex set of questions:

• Should you localize salaries for workers in different areas?
• How should you pay workers who have the same job when one is WFH and the other is at their desk?
• Are you being transparent with your staff about how their compensation is set?

News: SiriusXM launches ‘TikTok Radio,’ a music channel featuring viral hits hosted by TikTok stars

• August 20, 2021
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If viral TikTok songs like Dr. Dog’s “Where’d All the Time Go?” or Bo Burnham’s “Bezos I” weren’t already stuck in your head on loop, now they could be. Today SiriusXM launched a TikTok Radio channel, which features TikTok creators as channel hosts. The station is designed to sound like a “radio version of the

If viral TikTok songs like Dr. Dog’s “Where’d All the Time Go?” or Bo Burnham’s “Bezos I” weren’t already stuck in your head on loop, now they could be. Today SiriusXM launched a TikTok Radio channel, which features TikTok creators as channel hosts. The station is designed to sound like a “radio version of the platform’s ‘For You’ feed,” Sirius XM said.

SiriusXM, parent company to Pandora, announced this music channel in May, teasing the launch with curated Pandora playlists from influencers like Bella Poarch, whose lipsync video of Millie B’s “Soph Aspin Send [M to the B]” is the most liked video on TikTok.

With its TikTok partnership, SiriusXM is looking to capture a younger audience — on the TikTok app itself, DJ Habibeats (@djhabibeats) and DJ CONST (@erinconstantineofficial) will each go live on TikTok each week while DJing on TikTok Radio. Other creator hosts on TikTok Radio — like Billy (@8illy), Cat Haley (@itscathaley), HINDZ (@hindzsight), Lamar Dawson (@dirrtykingofpop) and Taylor Cassidy (@taylorcassidyj) — will deliver “The TikTok Radio Trending Ten,” a weekly countdown of songs trending on TikTok. To promote the station during its first week, artists like Ed Sheeran, Lil Nas X and Normani will appear on air.

Music has such a strong footing in TikTok culture that it regularly influences the Billboard charts — Fleetwood Mac’s “Rumours,” originally released in 1978, appeared in the top 10 Billboard albums again in 2020 after it was featured in a viral TikTok. Even a Fortnite-themed parody of Estelle’s “American Boy” — originally uploaded in 2018 to YouTube — had a beautiful moment on TikTok. 

“We’re so excited to launch TikTok Radio on SiriusXM, which opens up artists and creators like this amazing group of hosts to new audiences,” said Ole Obermann, TikTok’s global head of Music, in a statement. “Now SiriusXM subscribers will have a new road to discover the latest trends in music and get a first listen to tomorrow’s musical superstars. The channel captures song-breaking music culture that creates so much joy and entertainment on TikTok through video in an all-audio format.”

Though SiriusXM’s subscriber base continues to expand — it saw a 34% year-over-year growth from last year to now — it still dwarfs in comparison to streaming giants like Spotify, which has 165 million paid users. SiriusXM reported a total of 34.5 million subscribers as of Q2 this year, the most it’s ever had, but even Apple Music and Amazon Music have reported nearly double the subscribers. Pandora has 6.5 million paid subscribers. Over the last few years, SiriusXM and Pandora have struck deals with companies like SoundCloud, Simplecast and Stitcher to become more competitive in both music and podcast streaming. 

Still, other streaming companies have also shown interest in the market of Gen Z-ers on TikTok who want to listen to full versions of the catchy songs they hear in short videos. Apple Music and Spotify both host curated “viral hits” playlists. But a full-time satellite music channel is taking the trend a step further.

 

News: São Paulo’s QuintoAndar real estate platform raises $120M, now valued at $5.1B

• August 20, 2021
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Less than three months after announcing a $300 million Series E, Brazilian proptech QuintoAndar has raised an additional $120 million. New investors Greenoaks Capital and China’s Tencent co-led the round, which included participation from some existing backers as well. São Paulo-based QuintoAndar is now valued at $5.1 billion, up from $4 billion at the time

Less than three months after announcing a $300 million Series E, Brazilian proptech QuintoAndar has raised an additional $120 million.

New investors Greenoaks Capital and China’s Tencent co-led the round, which included participation from some existing backers as well. São Paulo-based QuintoAndar is now valued at $5.1 billion, up from $4 billion at the time of its last raise in late May. With the extension, the startup has now raised more than $700 million since its 2013 inception. Ribbit Capital led the first tranche of its Series E.

QuintoAndar describes itself as an “end-to-end solution for long-term rentals” that, among other things, connects potential tenants to landlords and vice versa. Last year, it also expanded into connecting home buyers to sellers. Its long-term plan is to ​​evolve into a one-stop real estate shop that also offers mortgage, title insurance and escrow services.

To that end, earlier this month, the startup acquired Atta Franchising, a 7-year-old São Paulo-based independent real estate mortgage broker. Specifically, acquiring Atta is designed speed up its ability to offer mortgage services to its users. QuintoAndar also plans to explore the possibility of offering a product to perform standalone transactions outside of its marketplace in partnership with other brokers, according to CEO and co-founder Gabriel Braga.

This year, QuintoAndar expanded operations into 14 new cities in Brazil. Eventually, QuintoAndar plans to enter the Mexican market as its first expansion outside of its home country but it has not yet set a date for that step. Today, the company has more than 120,000 rentals under management and about 10,000 new rentals per month. Its rental platform is live in 40 cities across Brazil, while its home-buying marketplace is live in four (Sao Paulo, Rio de Janeiro, Belo Horizonte and Porto Alegre) and seeing more than 10,000 sales in annualized terms.

QuintoAndar, he said, is open to acquiring more companies that it believes can either help it accelerate in a particular way or add something it had not yet thought about.

“We’re receptive to the idea but our core strategy is to focus on organic growth and our own innovation and accelerate that,” Braga said.

Why raise more money so soon?

The Series E was oversubscribed with investors who got in and “some who could not join,” according to Braga.

Greenoaks and Tencent, he said, couldn’t participate because of “timing issues.”

“We kept talking and they came back to us after the round, and wanted to be involved so we found a way to have them on board,” Braga said. “We did not need the money. But we have been constantly overachieving on the forecast that we shared with our investors. And that’s part of the reason why we had this extension.”

Greenoaks’ long-term time horizon was appealing because the firm’s investment was designed to be “perpetual capital with no predefined timeframe,” Braga said.

“We’re doing our best to build an enduring company that will be around for many, many years, so it’s good to have investors who share that vision and are technically aligned,” he added.

Greenoaks Partner Neil Shah said his firm believes that what QuintoAndar is building will “fundamentally reshape real estate transactions, enhancing transparency, expanding options for Brazilians seeking housing, dramatically simplifying the experience for landlords and driving increased investment into real estate across the country.” He also believes there is big potential for the company to take its offering to other parts of Latin America.

“We look forward to being partners for decades to come,” he added. 

Tencent’s experience in China is something QuintoAndar also finds valuable.

“We believe we can learn a lot from them and other Chinese companies doing interesting stuff there,” Braga said.

QuintoAndar isn’t the only Brazilian prop tech firm raising big money: In March, São Paulo digital real estate platform Loft announced it had closed on $425 million in Series D funding led by New York-based D1 Capital Partners. Then, about one month later, it revealed a $100 million extension that valued the company at $2.9 billion.

 

News: A mathematician walks into a bar (of disinformation)

• August 20, 2021
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Disinformation, misinformation, infotainment, algowars — if the debates over the future of media the past few decades have meant anything, they’ve at least left a pungent imprint on the English language. There’s been a lot of invective and fear over what social media is doing to us, from our individual psychologies and neurologies to wider

Disinformation, misinformation, infotainment, algowars — if the debates over the future of media the past few decades have meant anything, they’ve at least left a pungent imprint on the English language. There’s been a lot of invective and fear over what social media is doing to us, from our individual psychologies and neurologies to wider concerns about the strength of democratic societies. As Joseph Bernstein put it recently, the shift from “wisdom of the crowds” to “disinformation” has indeed been an abrupt one.

What is disinformation? Does it exist, and if so, where is it and how do we know we are looking at it? Should we care about what the algorithms of our favorite platforms show us as they strive to squeeze the prune of our attention? It’s just those sorts of intricate mathematical and social science questions that got Noah Giansiracusa interested in the subject.

Giansiracusa, a professor at Bentley University in Boston, is trained in mathematics (focusing his research in areas like algebraic geometry), but he’s also had a penchant of looking at social topics through a mathematical lens, such as connecting computational geometry to the Supreme Court. Most recently, he’s published a book called “How Algorithms Create and Prevent Fake News” to explore some of the challenging questions around the media landscape today and how technology is exacerbating and ameliorating those trends.

I hosted Giansiracusa on a Twitter Space recently, and since Twitter hasn’t made it easy to listen to these talks afterwards (ephemerality!), I figured I’d pull out the most interesting bits of our conversation for you and posterity.

This interview has been edited and condensed for clarity.

Danny Crichton: How did you decide to research fake news and write this book?

Noah Giansiracusa: One thing I noticed is there’s a lot of really interesting sociological, political science discussion of fake news and these types of things. And then on the technical side, you’ll have things like Mark Zuckerberg saying AI is going to fix all these problems. It just seemed like, it’s a little bit difficult to bridge that gap.

Everyone’s probably heard this recent quote of Biden saying, “they’re killing people,” in regards to misinformation on social media. So we have politicians speaking about these things where it’s hard for them to really grasp the algorithmic side. Then we have computer science people that are really deep in the details. So I’m kind of sitting in between, I’m not a real hardcore computer science person. So I think it’s a little easier for me to just step back and get the bird’s-eye view.

At the end of the day, I just felt I kind of wanted to explore some more interactions with society where things get messy, where the math is not so clean.

Crichton: Coming from a mathematical background, you’re entering this contentious area where a lot of people have written from a lot of different angles. What are people getting right in this area and what have people perhaps missed some nuance?

Giansiracusa: There’s a lot of incredible journalism; I was blown away at how a lot of journalists really were able to deal with pretty technical stuff. But I would say one thing that maybe they didn’t get wrong, but kind of struck me was, there’s a lot of times when an academic paper comes out, or even an announcement from Google or Facebook or one of these tech companies, and they’ll kind of mention something, and the journalist will maybe extract a quote, and try to describe it, but they seem a little bit afraid to really try to look and understand it. And I don’t think it’s that they weren’t able to, it really seems like more of an intimidation and a fear.

One thing I’ve experienced a ton as a math teacher is people are so afraid of saying something wrong and making a mistake. And this goes for journalists who have to write about technical things, they don’t want to say something wrong. So it’s easier to just quote a press release from Facebook or quote an expert.

One thing that’s so fun and beautiful about pure math, is you don’t really worry about being wrong, you just try ideas and see where they lead and you see all these interactions. When you’re ready to write a paper or give a talk, you check the details. But most of math is this creative process where you’re exploring, and you’re just seeing how ideas interact. My training as a mathematician you think would make me apprehensive about making mistakes and to be very precise, but it kind of had the opposite effect.

Second, a lot of these algorithmic things, they’re not as complicated as they seem. I’m not sitting there implementing them, I’m sure to program them is hard. But just the big picture, all these algorithms nowadays, so much of these things are based on deep learning. So you have some neural net, doesn’t really matter to me as an outsider what architecture they’re using, all that really matters is, what are the predictors? Basically, what are the variables that you feed this machine learning algorithm? And what is it trying to output? Those are things that anyone can understand.

Crichton: One of the big challenges I think of analyzing these algorithms is the lack of transparency. Unlike, say, the pure math world which is a community of scholars working to solve problems, many of these companies can actually be quite adversarial about supplying data and analysis to the wider community.

Giansiracusa: It does seem there’s a limit to what anyone can deduce just by kind of being from the outside.

So a good example is with YouTube — teams of academics wanted to explore whether the YouTube recommendation algorithm sends people down these conspiracy theory rabbit holes of extremism. The challenge is that because this is the recommendation algorithm, it’s using deep learning, it’s based on hundreds and hundreds of predictors based on your search history, your demographics, the other videos you’ve watched and for how long — all these things. It’s so customized to you and your experience, that all the studies I was able to find use incognito mode.

So they’re basically a user who has no search history, no information and they’ll go to a video and then click the first recommended video then the next one. And let’s see where the algorithm takes people. That’s such a different experience than an actual human user with a history. And this has been really difficult. I don’t think anyone has figured out a good way to algorithmically explore the YouTube algorithm from the outside.

Honestly, the only way I think you could do it is just kind of like an old-school study where you recruit a whole bunch of volunteers and sort of put a tracker on their computer and say, “Hey, just live life the way you normally do with your histories and everything and tell us the videos that you’re watching.” So it’s been difficult to get past this fact that a lot of these algorithms, almost all of them, I would say, are so heavily based on your individual data. We don’t know how to study that in the aggregate.

And it’s not just that me or anyone else on the outside who has trouble because we don’t have the data. It’s even people within these companies who built the algorithm and who know how the algorithm works on paper, but they don’t know how it’s going to actually behave. It’s like Frankenstein’s monster: they built this thing, but they don’t know how it’s going to operate. So the only way I think you can really study it is if people on the inside with that data go out of their way and spend time and resources to study it.

Crichton: There are a lot of metrics used around evaluating misinformation and determining engagement on a platform. Coming from your mathematical background, do you think those measures are robust?

Giansiracusa: People try to debunk misinformation. But in the process, they might comment on it, they might retweet it or share it, and that counts as engagement. So a lot of these measurements of engagement, are they really looking at positive or just all engagement? You know, it kind of all gets lumped together.

This happens in academic research, too. Citations are the universal metric of how successful research is. Well, really bogus things like Wakefield’s original autism and vaccines paper got tons of citations, a lot of them were people citing it because they thought it’s right, but a lot of it was scientists who were debunking it, they cite it in their paper to say, we demonstrate that this theory is wrong. But somehow a citation is a citation. So it all counts towards the success metric.

So I think that’s a bit of what’s happening with engagement. If I post something on my comments saying, “Hey, that’s crazy,” how does the algorithm know if I’m supporting it or not? They could use some AI language processing to try but I’m not sure if they are, and it’s a lot of effort to do so.

Crichton: Lastly, I want to talk a bit about GPT-3 and the concern around synthetic media and fake news. There’s a lot of fear that AI bots will overwhelm media with disinformation — how scared or not scared should we be?

Giansiracusa: Because my book really grew out of a class from experience, I wanted to try to stay impartial, and just kind of inform people and let them reach their own decisions. I decided to try to cut through that debate and really let both sides speak. I think the newsfeed algorithms and recognition algorithms do amplify a lot of harmful stuff, and that is devastating to society. But there’s also a lot of amazing progress of using algorithms productively and successfully to limit fake news.

There’s these techno-utopians, who say that AI is going to fix everything, we’ll have truth-telling, and fact-checking and algorithms that can detect misinformation and take it down. There’s some progress, but that stuff is not going to happen, and it never will be fully successful. It’ll always need to rely on humans. But the other thing we have is kind of irrational fear. There’s this kind of hyperbolic AI dystopia where algorithms are so powerful, kind of like singularity type of stuff that they’re going to destroy us.

When deep fakes were first hitting the news in 2018, and GPT-3 had been released a couple years ago, there was a lot of fear that, “Oh shit, this is gonna make all our problems with fake news and understanding what’s true in the world much, much harder.” And I think now that we have a couple of years of distance, we can see that they’ve made it a little harder, but not nearly as significantly as we expected. And the main issue is kind of more psychological and economic than anything.

So the original authors of GPT-3 have a research paper that introduces the algorithm, and one of the things they did was a test where they pasted some text in and expanded it to an article, and then they had some volunteers evaluate and guess which is the algorithmically-generated one and which article is the human-generated one. They reported that they got very, very close to 50% accuracy, which means barely above random guesses. So that sounds, you know, both amazing and scary.

But if you look at the details, they were extending like a one line headline to a paragraph of text. If you tried to do a full, The Atlantic-length or New Yorker-length article, you’re gonna start to see the discrepancies, the thought is going to meander. The authors of this paper didn’t mention this, they just kind of did their experiment and said, “Hey, look how successful it is.”

So it looks convincing, they can make these impressive articles. But here’s the main reason, at the end of the day, why GPT-3 hasn’t been so transformative as far as fake news and misinformation and all this stuff is concerned. It’s because fake news is mostly garbage. It’s poorly written, it’s low quality, it’s so cheap and fast to crank out, you could just pay your 16-year-old nephew to just crank out a bunch of fake news articles in minutes.

It’s not so much that math helped me see this. It’s just that somehow, the main thing we’re trying to do in mathematics is to be skeptical. So you have to question these things and be a little skeptical.

News: Spotify to spend $1B buying its own stock

• August 20, 2021
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If Spotify is still a growth-focused company, shouldn’t it preserve its capital to invest in exclusive podcasts and the like — efforts that may grant it pricing power in the future?

Music streaming service Spotify today said it will spend up to $1 billion between now and April 21, 2026 to repurchase its own shares. The dollar amount represents just under 2.5% of Spotify’s market cap, with the company valued at $41.06 billion this morning as its shares rose 5.1% following the repurchase news.

The company previously executed a similar buyback program in 2018.

A public company using some of its cash to repurchase its shares is nothing new. Many public companies, including Apple, Alphabet, and Microsoft, have active share repurchase programs, and it is common to see mature or nearly-mature companies devoting a fraction of their balance sheet or a regular percentage of their free cash flow to buying back their own equity.

The goal of such efforts is to return cash to shareholders. Buybacks, along with dividends, are among the key ways that companies can use their wealth to reward shareholders. Also, by buying their own stock, companies can boost the value of their individual shares. By limiting the shares in circulation, the company’s share count declines and the value of each share consequently rises, in theory, as it represents a larger fraction of ownership in the corporation.

Spotify shares have traded as high as $387.44 apiece in the past 12 months, but are now worth just $215.84, inclusive of today’s gains. From that perspective, seeing Spotify decide to deploy some cash to repurchase its own equity makes sense — the company is buying low.

But if you ask a recently public company what it intends to do with its excess cash, buybacks are not usually the answer. For example, TechCrunch asked Root Insurance CEO Alex Timm if his company intended to use cash reserves to purchase its own equity after its recent Q2 2021 earnings report. Root’s share price has declined in recent months, perhaps making it an attractive time to reward shareholders through buybacks. Timm demurred on the idea, saying instead that his company is building for the long-term. That translates to: That cash is earmarked for growth, not shareholder return.

But isn’t Spotify still a growth company? It certainly isn’t valued on the weight of its profits. In the first half of 2021, for example, Spotify posted net profit of a mere €3 million on revenues of €4.5 billion.

If Spotify is still a growth-focused company, shouldn’t it preserve its capital to invest in exclusive podcasts and the like — efforts that may grant it pricing power in the future and allow for stronger revenue growth and gross margins over time?

To answer that, we’ll have to check the company’s balance sheet. From its Q2 2021 earnings, here are the key numbers:

• Spotify closed out the second quarter with “€3.1 billion in cash and cash equivalents, restricted cash, and short term investments.”
• And in the second quarter, Spotify generated free cash flow of €34 million. That figure was up €7 million from a year earlier despite “higher working capital needs arising from select licensor payments (delayed from Q1), podcast-related payments, and higher ad-receivables”.

More simply, despite paying up for efforts that are generally understood to be key to Spotify’s long-term ability to improve its gross margins — and therefore its net profitability — the company is still throwing off cash. And with a huge bank account earning little, thanks to globally low prices for cash and equivalent holdings, Spotify is using a chunk of its funds to buy back stock.

By spending $1 billion over the next few years, Spotify won’t materially harm its cash position. Indeed, it will remain incredibly cash-rich. However, the move may help defend its valuation and keep itchy investors happy. Moreover, as the company is buying its stock at a firm discount to where the market valued it recently, it could get something akin to a deal, given Spotify’s long-term faith in the value of its own business.

Perhaps the better question as this juncture is not whether Spotify is a weird company for deciding to break off a piece of its wealth for shareholders, but instead why we aren’t seeing other breakeven-ish tech companies with neutral cash flows and fat accounts doing the same.

News: Nvidia-ARM takeover raises serious antitrust concerns, finds UK’s CMA

• August 20, 2021
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The UK’s competition watchdog has raised serious concerns about Nvidia’s proposed takeover of chip designer, ARM. Its assessment was published today by the government which will now need to decide whether to ask the Competition and Markets Authority (CMA) to carry out an in-depth probe into the proposed acquisition. In the executive summary of the

The UK’s competition watchdog has raised serious concerns about Nvidia’s proposed takeover of chip designer, ARM.

Its assessment was published today by the government which will now need to decide whether to ask the Competition and Markets Authority (CMA) to carry out an in-depth probe into the proposed acquisition.

In the executive summary of the CMA’s report for the government the watchdog sets out concerns that if the deal were to go ahead the merged business would have the ability and incentive to harm the competitiveness of Nvidia’s rivals by restricting access to ARM’s IP which is used by companies that produce semiconductor chips and related products, in competition with Nvidia.

The CMA is worried that the loss of competition could stifle innovation across a number of markets — including data centres, gaming, the ‘internet of things’, and self-driving cars, with the resulting risk of more expensive or lower quality products for businesses and consumers.

A behavioral remedy offered by Nvidia was rejected by the CMA — which has recommended moving to an in-depth ‘Phase 2’ investigation of the proposed merger on competition grounds. 

Commenting in a statement, CEO Andrea Coscelli said: “We’re concerned that Nvidia controlling Arm could create real problems for NVIDIA’s rivals by limiting their access to key technologies, and ultimately stifling innovation across a number of important and growing markets. This could end up with consumers missing out on new products, or prices going up.

“The chip technology industry is worth billions and is vital to products that businesses and consumers rely on every day. This includes the critical data processing and datacentre technology that supports digital businesses across the economy, and the future development of artificial intelligence technologies that will be important to growth industries like robotics and self-driving cars.”

Nvidia has been contacted for comment.

In a statement on its website, the Department for Digital, Media, Culture and Sport said the UK’s digital secretary is now “considering the relevant information contained in the full report” and will make a decision on whether to ask the CMA to conduct a ‘Phase Two’ investigation “in due course”.

“There is no set period in which this decision must be made, but it must take into account the need to make a decision as soon as reasonably practicable to reduce uncertainty,” it added. 

The proposed merger has faced considerable domestic opposition with opponents including one of the co-founders of ARM calling for it to be blocked.

News: Bird shows improving scooter economics, long march to profitability

• August 20, 2021
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Newly reported financial data from Bird, an American scooter sharing service, shows a company with an improving economic model, and a multi-year path to profitability. However, that path is fraught.

Newly reported financial data from Bird, an American scooter sharing service, shows a company with an improving economic model, and a multi-year path to profitability. However, that path is fraught unless a number of scenarios all work out, in concert and without a glitch.

Bird, well-known for its early battles with domestic rival Lime, is pursuing a SPAC-led deal that will see it go public and raise fresh capital. The former startup is merging with Switchback II Corporation in a deal that values it at around $2.3 billion, including a $160 million PIPE (private investment in public equity) component. (Note: The group purchasing TechCrunch’s parent company from its own parent company, is part of the Bird PIPE.)

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COVID-19 hasn’t been kind to Bird and similar companies around the world. As many around the world stayed home, usage of shared-asset services and ride-hail applications fell sharply. Bird saw rides decline. Airbnb took a temporary hit. Uber and Lyft saw ride demand fall.

Responses to the crisis were varied. Airbnb cut costs, and raised external capital. Lyft cut expenses and focused on its core model, while Uber grew its food delivery business, which saw transaction volume soar as demand fell for its traditional business.

Meanwhile, Bird flipped its entire business model. That decision has helped the scooter outfit improve its economics markedly, giving it a shot at generating profit in the future — provided its forecasts prove achievable.

This morning, let’s talk about how Bird has changed its business, their impacts on its operating results, and how long the company thinks its climb to profitability is.

Fleet management → Fleet managers

In their initial forms, Bird and Lime bought and deployed large fleets of electric scooters. Not only was this capital intensive, the companies also wound up with costs that were more than sticky — charging wasn’t simple or cheap, moving scooters around to balance demand took both human capital and vehicles, and the list went on.

Throw in vehicle depreciation — the pace at which scooters in the wild degraded from use or abuse — and the businesses proved excellent vehicles for raising capital and throwing that money at more scooters, costs, and, as it turned out, losses.

Results improved somewhat over time, though. As scooter-share companies increasingly built their own hardware, their economics improved. Sturdier scooters meant lower depreciation, and better battery tech could allow for more rides per charge. That sort of thing.

But the model wasn’t incredibly lucrative even before COVID-19 hit. Costs were high, and the model did not break even even on a gross margin basis, let alone when considering all corporate expenses. You can see the financial mess from that period of operations in historical Bird results.

News: Men are a niche demographic

• August 20, 2021
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Hello and welcome back to Equity, TechCrunch’s venture capital-focused podcast, where we unpack the numbers behind the headlines. Danny was back, joining Natasha and Alex and Grace and Chris to chat through the week’s coming and goings. But, before we get to the official news, here’s some personal news: Danny is stepping back from his role as co-host of the Friday show! Yes, Mr.

Hello and welcome back to Equity, TechCrunch’s venture capital-focused podcast, where we unpack the numbers behind the headlines.

Danny was back, joining Natasha and Alex and Grace and Chris to chat through the week’s coming and goings. But, before we get to the official news, here’s some personal news: Danny is stepping back from his role as co-host of the Friday show! Yes, Mr. Crichton will still take part in our mid-week, deep dive episodes, but this is the conclusion of his run as part of the news roundup. We will miss him, glad that his transitions and wit will continue to be part of the Equity universe.

Who will take the third chair? Well, stay tuned. We have some neat things planned.

Now, the rundown:

• Funding rounds: Maven has built a women’s health unicorn, Monte Carlo raised $60 million for data observability, and Launch House wants to scale venture community with a fresh $3 million in its accounts. The last round is probably the most controversial one of them all, so each of us took a side and discussed what’s new and old about hacker homes.
• The next crop of key IPOs: Please say hello to the rising seniors of the startup world, companies that are the next IPOs that we are excited about. The list includes Discord, Databricks, Chime and Carta, which made headlines this week after setting its own valuation with its own tool. Will investors and startups turn to a third-party to value companies? What happens if secondary investors aren’t as into your product as you are? We had a ton of questions.
• Brazil’s burgeoning startup and exit market: In the wake of Nuvemshop raising a zillion dollars, it was time to sit down and talk about Brazil. Alex and Anna Heim have been rigorous in their reporting on the fascinating exit market. Who knew dual-listings were so dramatic?
• After traveling overseas, we went very close to home to speak about the news industry. Danny had a piece about informed., which a trio of media veterans believe could fix the economics that plague subscription-based publications. The nuts and bolts are in the episode, but prepare to debate if you’re the kind of reader that likes a snack, or the whole lip smackin’ meal.
• Finally, we discuss the wack reality that YikYak is indeed back. 

News: Cardiomatics bags $3.2M for its ECG-reading AI

• August 20, 2021
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Poland-based healthtech AI startup Cardiomatics has announced a $3.2M seed raise to expand use of its electrocardiogram (ECG) reading automation technology. The round is led by Central and Eastern European VC Kaya, with Nina Capital, Nova Capital and Innovation Nest also participating. The seed raise also includes a $1M non-equity grant from the Polish National

Poland-based healthtech AI startup Cardiomatics has announced a $3.2M seed raise to expand use of its electrocardiogram (ECG) reading automation technology.

The round is led by Central and Eastern European VC Kaya, with Nina Capital, Nova Capital and Innovation Nest also participating.

The seed raise also includes a $1M non-equity grant from the Polish National Centre of Research and Development.

The 2017-founded startup sells a cloud tool to speed up diagnosis and drive efficiency for cardiologists, clinicians and other healthcare professionals to interpret ECGs — automating the detection and analyse of some 20 heart abnormalities and disorders with the software generating reports on scans in minutes, faster than a trained human specialist would be able to work.

Cardiomatics touts its tech as helping to democratize access to healthcare — saying the tool enables cardiologists to optimise their workflow so they can see and treat more patients. It also says it allows GPs and smaller practices to offer ECG analysis to patients without needing to refer them to specialist hospitals.

The AI tool has analyzed more than 3 million hours of ECG signals commercially to date, per the startup, which says its software is being used by more than 700 customers in 10+ countries, including Switzerland, Denmark, Germany and Poland.

The software is able to integrate with more than 25 ECG monitoring devices at this stage, and it touts offering a modern cloud software interface as a differentiator vs legacy medical software.

Asked how the accuracy of its AI’s ECG readings has been validated, the startup told us: “The data set that we use to develop algorithms contains more than 10 billion heartbeats from approximately 100,000 patients and is systematically growing. The majority of the data-sets we have built ourselves, the rest are publicly available databases.

“Ninety percent of the data is used as a training set, and 10% for algorithm validation and testing. According to the data-centric AI we attach great importance to the test sets to be sure that they contain the best possible representation of signals from our clients. We check the accuracy of the algorithms in experimental work during the continuous development of both algorithms and data with a frequency of once a month. Our clients check it everyday in clinical practice.”

Cardiomatics said it will use the seed funding to invest in product development, expand its business activities in existing markets and gear up to launch into new markets.

“Proceeds from the round will be used to support fast-paced expansion plans across Europe, including scaling up our market-leading AI technology and ensuring physicians have the best experience. We prepare the product to launch into new markets too. Our future plans include obtaining FDA certification and entering the US market,” it added.

The AI tool received European medical device certification in 2018 — although it’s worth noting that the European Union’s regulatory regime for medical devices and AI is continuing to evolve, with an update to the bloc’s Medial Devices Directive (now known as the EU Medical Device Regulation) coming into application earlier this year (May).

A new risk-based framework for applications of AI — aka the Artificial Intelligence Act — is also incoming and will likely expand compliance demands on AI healthtech tools like Cardiomatics, introducing requirements such as demonstrating safety, reliability and a lack of bias in automated results.

Asked about the regulatory landscape it said: “When we launched in 2018 we were one of the first AI-based solutions approved as medical device in Europe. To stay in front of the pace we carefully observe the situation in Europe and the process of legislating a risk-based framework for regulating applications of AI. We also monitor draft regulations and requirements that may be introduced soon. In case of introducing new standards and requirements for artificial intelligence, we will immediately undertake their implementation in the company’s and product operations, as well as extending the documentation and algorithms validation with the necessary evidence for the reliability and safety of our product.”

However it also conceded that objectively measuring efficacy of ECG reading algorithms is a challenge.

“An objective assessment of the effectiveness of algorithms can be very challenging,” it told TechCrunch. “Most often it is performed on a narrow set of data from a specific group of patients, registered with only one device. We receive signals from various groups of patients, coming from different recorders. We are working on this method of assessing effectiveness. Our algorithms, which would allow them to reliably evaluate their performance regardless of various factors accompanying the study, including the recording device or the social group on which it would be tested.”

“When analysis is performed by a physician, ECG interpretation is a function of experience, rules and art. When a human interprets an ECG, they see a curve. It works on a visual layer. An algorithm sees a stream of numbers instead of a picture, so the task becomes a mathematical problem. But, ultimately, you cannot build effective algorithms without knowledge of the domain,” it added. “This knowledge and the experience of our medical team are a piece of art in Cardiomatics. We shouldn’t forget that algorithms are also trained on the data generated by cardiologists. There is a strong correlation between the experience of medical professionals and machine learning.”

News: Greycroft leads $3.5M into Breef, an online marketplace for ad agencies

• August 20, 2021
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Breef’s platform is democratizing how brands and boutique agencies connect with each other in the process of planning, scoping, pitching and paying for projects.

Breef raised $3.5 million in funding to continue developing what it boasts as “the world’s first online marketplace” for transactions between brands and agencies.

Greycroft led the round and was joined by Rackhouse Ventures, The House Fund, John and Helen McBain, Lance Armstrong and 640 Oxford Ventures. Including the new round, the New York and Colorado-based company has brought in total funding of $4.5 million since its inception in 2019 by husband-and-wife co-founders George Raptis and Emily Bibb.

Bibb’s background is in digital marketing and brand building at companies like PopSugar, VSCO and S’well, while Raptis was on the founding team at fintech company Credible.com.

Both said they experienced challenges in finding agencies, which traditionally involved asking for referrals and then making a bunch of calls. There were also times when their companies would be in high demand for talent, but didn’t necessarily need a full-time employee to achieve the goal or project milestone.

While the concept of outsourcing is not new, Breef’s differentiator is its ability to manage complex projects: a traditional individual freelance project is less than $1,000 and takes a week or less. Instead, the company is working with team-based projects that average $25,000 with a length of engagement of about six months, Raptis said.

Breef’s platform is democratizing how brands and boutique agencies connect with each other in the process of planning, scoping, pitching and paying for projects, Raptis told TechCrunch.

“At the core, we are taking the agency online,” Bibb added. “We are building a platform to streamline a complicated process for outsourcing high-value work and allow users to find, pay for and work with agencies in days rather than months.”

Brands can draft their own brief to articulate what they need, and Breef will connect them to a short list of agencies that match those requirements. Rather than a one- or two-month search, the company is able to bring that down to five days.

Bibb and Raptis decided to seek venture capital after experiencing demand — millions of dollars in projects are being created on the platform each month — and some tailwinds from the shift to remote work. They saw many brands that may have originally utilized in-house teams or agencies of record turn to distributed or smaller teams.

Due to the nature of agency work being expensive, Breef is processing large amounts of money over the internet, and the founders want to continue developing the technology and hiring talent so that it is a secure and trustworthy system.

It also launched its buy now, pay later project funding service, Breef(pay), to streamline payments to agencies and reduce cash flow challenges. Users can construct their own payment terms, mix up the way they are paid and utilize a credit line or defer payments to control external spend.

To date, Breef has more than 5,000 vetted boutique agencies in 20 countries on its platform and is able to save its users an average of 32% in product costs compared with a traditional agency model. It boasts a customer list that includes Spotify, Brex, Shutterstock, Bluestone Lane and Kinrgy.

Kevin Novak, founder of Rackhouse Ventures, said he met Raptis through the Australian tech community. He recently launched his first fund targeting startups in novel applications of data.

“When they were talking to me about what they wanted to do, I got intrigued,” Novak said. “I like finding marketplaces where the idea is well understood by the people involved. Looking at the matching problem, Emily and George have found a unique way to find ad agencies that hasn’t existed before.”