Today, Amazon Web Services is a mainstay in the cloud infrastructure services market, a $60 billion juggernaut of a business. But in 2008, it was still new, working to keep its head above water and handle growing demand for its cloud servers. In fact, 15 years ago last week, the company launched Amazon EC2 in beta. From that point forward, AWS offered startups unlimited compute power, a primary selling point at the time.
EC2 was one of the first real attempts to sell elastic computing at scale — that is, server resources that would scale up as you needed them and go away when you didn’t. As Jeff Bezos said in an early sales presentation to startups back in 2008, “you want to be prepared for lightning to strike, […] because if you’re not that will really generate a big regret. If lightning strikes, and you weren’t ready for it, that’s kind of hard to live with. At the same time you don’t want to prepare your physical infrastructure, to kind of hubris levels either in case that lightning doesn’t strike. So, [AWS] kind of helps with that tough situation.”
An early test of that value proposition occurred when one of their startup customers, Animoto, scaled from 25,000 to 250,000 users in a 4-day period in 2008 shortly after launching the company’s Facebook app at South by Southwest.
At the time, Animoto was an app aimed at consumers that allowed users to upload photos and turn them into a video with a backing music track. While that product may sound tame today, it was state of the art back in those days, and it used up a fair amount of computing resources to build each video. It was an early representation of not only Web 2.0 user-generated content, but also the marriage of mobile computing with the cloud, something we take for granted today.
For Animoto, launched in 2006, choosing AWS was a risky proposition, but the company found trying to run its own infrastructure was even more of a gamble because of the dynamic nature of the demand for its service. To spin up its own servers would have involved huge capital expenditures. Animoto initially went that route before turning its attention to AWS because it was building prior to attracting initial funding, Brad Jefferson, co-founder and CEO at the company explained.
“We started building our own servers, thinking that we had to prove out the concept with something. And as we started to do that and got more traction from a proof-of-concept perspective and started to let certain people use the product, we took a step back, and were like, well it’s easy to prepare for failure, but what we need to prepare for success,” Jefferson told me.
Going with AWS may seem like an easy decision knowing what we know today, but in 2007 the company was really putting its fate in the hands of a mostly unproven concept.
“It’s pretty interesting just to see how far AWS has gone and EC2 has come, but back then it really was a gamble. I mean we were talking to an e-commerce company [about running our infrastructure]. And they’re trying to convince us that they’re going to have these servers and it’s going to be fully dynamic and so it was pretty [risky]. Now in hindsight, it seems obvious but it was a risk for a company like us to bet on them back then,” Jefferson told me.
Animoto had to not only trust that AWS could do what it claimed, but also had to spend six months rearchitecting its software to run on Amazon’s cloud. But as Jefferson crunched the numbers, the choice made sense. At the time, Animoto’s business model was for free for a 30 second video, $5 for a longer clip, or $30 for a year. As he tried to model the level of resources his company would need to make its model work, it got really difficult, so he and his co-founders decided to bet on AWS and hope it worked when and if a surge of usage arrived.
That test came the following year at South by Southwest when the company launched a Facebook app, which led to a surge in demand, in turn pushing the limits of AWS’s capabilities at the time. A couple of weeks after the startup launched its new app, interest exploded and Amazon was left scrambling to find the appropriate resources to keep Animoto up and running.
Dave Brown, who today is Amazon’s VP of EC2 and was an engineer on the team back in 2008, said that “every [Animoto] video would initiate, utilize and terminate a separate EC2 instance. For the prior month they had been using between 50 and 100 instances [per day]. On Tuesday their usage peaked at around 400, Wednesday it was 900, and then 3,400 instances as of Friday morning.” Animoto was able to keep up with the surge of demand, and AWS was able to provide the necessary resources to do so. Its usage eventually peaked at 5000 instances before it settled back down, proving in the process that elastic computing could actually work.
At that point though, Jefferson said his company wasn’t merely trusting EC2’s marketing. It was on the phone regularly with AWS executives making sure their service wouldn’t collapse under this increasing demand. “And the biggest thing was, can you get us more servers, we need more servers. To their credit, I don’t know how they did it — if they took away processing power from their own website or others — but they were able to get us where we needed to be. And then we were able to get through that spike and then sort of things naturally calmed down,” he said.
The story of keeping Animoto online became a main selling point for the company, and Amazon was actually the first company to invest in the startup besides friends and family. It raised a total of $30 million along the way, with its last funding coming in 2011. Today, the company is more of a B2B operation, helping marketing departments easily create videos.
While Jefferson didn’t discuss specifics concerning costs, he pointed out that the price of trying to maintain servers that would sit dormant much of the time was not a tenable approach for his company. Cloud computing turned out to be the perfect model and Jefferson says that his company is still an AWS customer to this day.
While the goal of cloud computing has always been to provide as much computing as you need on demand whenever you need it, this particular set of circumstances put that notion to the test in a big way.
Today the idea of having trouble generating 3,400 instances seems quaint, especially when you consider that Amazon processes 60 million instances every day now, but back then it was a huge challenge and helped show startups that the idea of elastic computing was more than theory.
Over the previous two or three years we’ve seen an explosion of new debit and credit card products come to market from consumer and B2B fintech startups, as well as companies that we might not traditionally think of as players in the financial services industry.
On the consumer side, that means companies like Venmo or PayPal offering debit cards as a new way for users to spend funds in their accounts. In the B2B space, the availability of corporate card issuing by startups like Brex and Ramp has ushered in new expense and spend management options. And then there is the growth of branded credit and debit cards among brands and sports teams.
But if your company somehow hasn’t yet found its way to launch a debit or credit card, we have good news: It’s easier than ever to do so and there’s actual money to be made. Just know that if you do, you’ve got plenty of competition and that actual customer usage will probably depend on how sticky your service is and how valuable the rewards are that you offer to your most active users.
To learn more about launching a card product, TechCrunch spoke with executives from Marqeta, Expensify, Synctera and Cardless about the pros and cons of launching a card product. So without further ado, here are all the reasons you should think about doing so, and one big reason why you might not want to.
Probably the biggest reason we’ve seen so many new fintech and non-fintech companies rush to offer debit and credit cards to customers is simply that it’s easier than ever for them to do so. The launch and success of businesses like Marqeta has made card issuance by API developer friendly, which lowered the barrier to entry significantly over the last half-decade.
“The reason why this is happening is because the ‘fintech 1.0 infrastructure’ has succeeded,” Salman Syed, Marqeta’s SVP and GM of North America, said. “When you’ve got companies like [ours] out there, it’s just gotten a lot easier to be able to put a card product out.”
While noting that there have been good options for card issuance and payment processing for at least the last five or six years, Expensify Chief Operating Officer Anu Muralidharan said that a proliferation of technical resources for other pieces of fintech infrastructure has made the process of greenlighting a card offering much easier over the years.
Itamar Jobani was a software developer working for a medical company and “hated that time of the month” when he had to use the company’s chosen reimbursement tool.
“It was full of friction and as part of the company’s wellness team, I felt an urge to take care of the employee experience and find a better tool,” Jobani told TechCrunch. “I looked for something, but didn’t find it, so I tried to build it myself.”
What resulted was PayEm, an Israeli company he founded with Omer Rimoch in 2019 to be a spend and procurement platform for high-growth and multinational organizations. Today, it announced $27 million in funding that includes $7 million in seed funding, led by Pitango First and NFX, with participation by LocalGlobe and Fresh Fund, as well as $20 million in Series A funding led by Glilot+.
The company’s technology automates the reimbursement, procurement, accounts payable and credit card workflows to manage all of the requests and invoices, while also creating bills and sending payments to over 200 territories in 130 currencies.
It gives company finance teams a real-time look at what items employees are asking for funds to buy, and what is actually being spent. For example, teams can submit a request and go through an approval flow that can be customized with purchasing codes tied to a description of the transaction. At the same time, all transactions are continuously reconciled versus having to spend hours at the end of the month going through paperwork.
“Organizations are running in a more democratized way with teams buying things on behalf of the organization,” Jobani said. “We built a platform to cater to those needs, so it’s like a disbursement platform instead of a finance team always being in charge.”
The global B2B payments market is valued at $120 trillion annually and is expected to reach $200 trillion by 2028, according to payment industry newsletter Nilson Report. PayEm is among many B2B payments startups attracting venture capital — for example, last month, Nium announced a $200 million in Series D funding at a $1 billion valuation. Paystand raised $50 million in Series C funding to make B2B payments cashless, while Dwolla raised $21 million for its API that allows companies to build and facilitate fast payments.
Meanwhile, PayEm itself saw accelerated growth in the second quarter of 2021, including increasing its transaction volume by four times over the previous quarter and generating millions of dollars in revenue. It now boasts a list of hundreds of customers like Fiverr, JFrog and Next Insurance. It also launched new features like the ability to create corporate cards.
The company, which also has an office in New York, has 40 employees currently, and the new funds will enable the company to triple its headcount, focusing on hiring in the United States, and to bring additional features and payment capabilities to market.
“Each person can have a budget and a time frame for making the purchase, while accounting still feels in control,” Jobani added. “Everyone now has the full context and the right budget line item.”
On the heels of Heroes announcing a $200 million raise earlier today, to double down on buying and scaling third-party Amazon Marketplace sellers, another startup out of London aiming to do the same is announcing some significant funding of its own. Olsam, a roll-up play that is buying up both consumer and B2B merchants selling on Amazon by way of Amazon’s FBA fulfillment program, has closed $165 million — a combination of equity and debt that it will be using to fuel its M&A strategy, as well as continue building out its tech platform and to hire more talent.
Apeiron Investment Group — an investment firm started by German entrepreneur Christian Angermayer (known first for biopharmaceuticals, then investing and crypto, including playing a role in SoftBank investing in Wirecard) — led the Series A equity round, with Elevat3 Capital (another Angermayer firm that has a strategic partnership with Founders Fund and Peter Thiel) also participating. North Wall Capital was behind the debt portion of the deal. We have asked and Olsam is only disclosing the full amount raised, not the amount that was raised in equity versus debt. Valuation is also not being disclosed.
Being an Amazon roll-up startup from London that happens to be announcing a fundraise today is not the only thing that Olsam has in common with Heroes. Like Heroes, Olsam is also founded by brothers.
Sam Horbye previously spent years working at Amazon, including building and managing the company’s Business Marketplace (the B2B version of the consumer Marketplace); while co-founder Ollie Horbye had years of experience in strategic consulting and financial services.
Between them, they had also built and sold previous marketplace businesses, and they believe that this collective experience gives Olsam — a portmanteau of their names, “Ollie” and “Sam” — a leg up when it comes to building relationships with merchants; identifying quality products (versus the vast seas of search results that often feel like they are selling the same inexpensive junk as each other); and understanding merchants’ challenges and opportunities, and building relationships with Amazon and understanding how the merchant ecosystem fits into the e-commerce giant’s wider strategy.
Olsam is also taking a slightly different approach when it comes to target companies, by focusing not just on the usual consumer play, but also on merchants selling to businesses. B2B selling is currently one of the fastest-growing segments in Amazon’s Marketplace, and it is also one of the more overlooked by consumers.”It’s flying under the radar,” Ollie said.
“The B2B opportunity is very exciting,” Sam added. “A growing number of merchants are selling office supplies or more random products to the B2B customer.”
Estimates vary when it comes to how many merchants there are selling on Amazon’s Marketplace globally, ranging anywhere from 6 million to nearly 10 million. Altogether those merchants generated $300 million in sales (gross merchandise value), and its growing by 50% each year at the moment.
And consolidating sellers — in order to achieve better economies of scale around supply chains, marketing tools and analytics, and more — is also big business. Olsam estimates that some $7 billion has been spent cumulatively on acquiring these businesses, and there are more out there: Olsam estimates that there are some 3,000 businesses in the UK alone making more than $1 million each in sales on Amazon’s platform.
(And to be clear, there are a number of other roll-up startups beyond Heroes also eyeing up that opportunity. Raising hundreds of millions of dollars in aggregate, others have made moves this year include Suma Brands ($150 million); Elevate Brands ($250 million); Perch ($775 million); factory14 ($200 million); Thrasio (currently probably the biggest of them all in terms of reach and money raised and ambitions), Heyday, The Razor Group, Branded, SellerX, Berlin Brands Group (X2), Benitago, Latin America’s Valoreo and Rainforest and Una Brands out of Asia.)
“The senior team behind Olsam is what makes this business truly unique,” said Angermayer in a statement. “Having all been successful in building and selling their own brands within the market and having worked for Amazon in their marketplace team – their understanding of this space is exceptional.”
The digital transformation currently sweeping society has likely reached your favorite local restaurant.
Since 2013, Boston-based Toast has offered bars and eateries a software platform that lets them manage orders, payments and deliveries.
Over the last year, its customers have processed more than $38 billion in gross payment volume, so Alex Wilhelm analyzed the company’s S-1 for The Exchange with great interest.
“Toast was last valued at just under $5 billion when it last raised, per Crunchbase data,” he writes. “And folks are saying that it could be worth $20 billion in its debut. Does that square with the numbers?”
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Airbnb, DoorDash and Coinbase each debuted at past Y Combinator Demo Days; as of this writing, they employ a combined 10,000 people.
Today and tomorrow, TechCrunch reporters will cover the proceedings at YC’s Summer 20201 Demo Day. In addition to writing up founder pitches, they’ll also rank their favorites.
Even remotely, I can feel a palpable sense of excitement radiating from our team — anything can happen at YC Demo Day, so sign up for Extra Crunch to follow the action.
Thanks very much for reading; I hope you have an excellent week.
Senior Editor, TechCrunch
Image Credits: Ron Miller/TechCrunch
In August 2006, AWS activated its EC2 cloud-based virtual computer, a milestone in the cloud infrastructure giant’s development.
“You really can’t overstate what Amazon was able to accomplish,” writes enterprise reporter Ron Miller.
In the 15 years since, EC2 has enabled clients of any size to test and run their own applications on AWS’ virtual machines.
To learn more about a fundamental technological shift that “would help fuel a whole generation of startups,” Ron interviewed EC2 VP Dave Brown, who built and led the Amazon EC2 Frontend team.
Image Credits: Jasmin Merdan (opens in a new window)/ Getty Images
Most managers agree that OKRs foster transparency and accountability, but running a team effectively has different challenges when workers are attending all-hands meetings from their kitchen tables.
Instead of just discussing key metrics before board meetings or performance reviews, make them part of the day-to-day culture, recommends Jeremy Epstein, Gtmhub’s CMO.
“Strengthen your team by creating authentic workplace transparency using numbers as a universal language and providing meaning behind your team’s work.”
Image Credits: Getty Images under an Andrii Yalanskyi (opens in a new window) license
Many founders must overcome a few emotional hurdles before they’re comfortable pitching a potential investor face-to-face.
To alleviate that pressure, Unicorn Capital founder Evan Fisher recommends that entrepreneurs use pre-pitch meetings to build and strengthen relationships before asking for a check:
“This is the ‘we actually aren’t looking for money; we just want to be friends for now’ pitch that gets you on an investor’s radar so that when it’s time to raise your next round, they’ll be far more likely to answer the phone because they actually know who you are.”
Pre-pitches are good for more than curing the jitters: These conversations help founders get a better sense of how VCs think and sometimes lead to serendipitous outcomes.
“Investors are opportunists by necessity,” says Fisher, “so if they like the cut of your business’s jib, you never know — the FOMO might start kicking hard.”
Image Credits: MirageC (opens in a new window) / Getty Images
FischerJordan’s Deeba Goyal and Archita Bhandari break down the pandemic’s impact on alternative lenders, specifically what they had to do to survive the crisis, taking a look at smaller lenders including Credibly, Kabbage, Kapitus and BlueVine.
“Only those who were able to find a way through the complexities of their existing capital sources were able to maintain their performance, and the rest were left to perish or find new funding avenues,” they write.
Image Credits: Nigel Sussman (opens in a new window)
Customer engagement software company Freshworks’ S-1 filing depicts a company that’s experiencing accelerating revenue growth, “a great sign for the health of its business,” reports Alex Wilhelm in this morning’s The Exchange.
“Most companies see their growth rates decline as they scale, as larger denominators make growth in percentage terms more difficult.”
Studying the company’s SEC filing, he found that “Freshworks isn’t a company where we need to cut it lots of slack, as we might with an adjusted EBITDA number. It is going public ready for Big Kid metrics.”
Fifteen years ago this week on August 25, 2006, AWS turned on the very first beta instance of EC2, its cloud-based virtual computers. Today cloud computing, and more specifically infrastructure as a service, is a staple of how businesses use computing, but at that moment it wasn’t a well known or widely understood concept.
The EC in EC2 stands for Elastic Compute, and that name was chosen deliberately. The idea was to provide as much compute power as you needed to do a job, then shut it down when you no longer needed it — making it flexible like an elastic band. The launch of EC2 in beta was preceded by the beta release of S3 storage six months earlier, and both services marked the starting point in AWS’ cloud infrastructure journey.
You really can’t overstate what Amazon was able to accomplish with these moves. It was able to anticipate an entirely different way of computing and create a market and a substantial side business in the process. It took vision to recognize what was coming and the courage to forge ahead and invest the resources necessary to make it happen, something that every business could learn from.
The AWS origin story is complex, but it was about bringing the IT power of the Amazon business to others. Amazon at the time was not the business it is today, but it was still rather substantial and still had to deal with massive fluctuations in traffic such as Black Friday when its website would be flooded with traffic for a short but sustained period of time. While the goal of an e-commerce site, and indeed every business, is attracting as many customers as possible, keeping the site up under such stress takes some doing and Amazon was learning how to do that well.
Those lessons and a desire to bring the company’s internal development processes under control would eventually lead to what we know today as Amazon Web Services, and that side business would help fuel a whole generation of startups. We spoke to Dave Brown, who is VP of EC2 today, and who helped build the first versions of the tech, to find out how this technological shift went down.
The genesis of the idea behind AWS started in the 2000 timeframe when the company began looking at creating a set of services to simplify how they produced software internally. Eventually, they developed a set of foundational services — compute, storage and database — that every developer could tap into.
But the idea of selling that set of services really began to take shape at an executive offsite at Jeff Bezos’ house in 2003. A 2016 TechCrunch article on the origins AWS described how that started to come together:
As the team worked, Jassy recalled, they realized they had also become quite good at running infrastructure services like compute, storage and database (due to those previously articulated internal requirements). What’s more, they had become highly skilled at running reliable, scalable, cost-effective data centers out of need. As a low-margin business like Amazon, they had to be as lean and efficient as possible.
They realized that those skills and abilities could translate into a side business that would eventually become AWS. It would take a while to put these initial ideas into action, but by December 2004, they had opened an engineering office in South Africa to begin building what would become EC2. As Brown explains it, the company was looking to expand outside of Seattle at the time, and Chris Pinkham, who was director in those days, hailed from South Africa and wanted to return home.
“Even with its vast local talent, it seems Israel still has many hurdles to overcome in order to become a global fintech hub. [ … ] Having that said, I don’t believe any of these obstacles will prevent Israel from generating disruptive global fintech startups that will become game-changing businesses.”
I wrote that back in 2018, when I was determined to answer whether Israel had the potential to become a global fintech hub. Suffice to say, this prediction from three years ago has become a reality.
In 2019, Israeli fintech startups raised over $1.8 billion; in 2020, they were said to have raised $1.48 billion despite the pandemic. Just in the first quarter of 2021, Israeli fintech startups raised $1.1 billion, according to IVC Research Center and Meitar Law Offices.
It’s then no surprise that Israel now boasts over a dozen fintech unicorns in sectors such as payments, insurtech, lending, banking and more, some of which reached the desired status just in the beginning of 2021 — like Melio and Papaya Global, which raised $110 million and $100 million, respectively.
Over the years I’ve been fortunate to invest (both as a venture capitalist and personally) in successful early-stage fintech companies in the U.S., Israel and emerging markets — Alloy, Eave, MoneyLion, Migo, Unit, AcroCharge and more.
The major shifts and growth of fintech globally over these years has been largely due to advanced AI-based technologies, heightened regulatory scrutiny, a more innovative and adaptive approach among financial institutions to build partnerships with fintechs, and, of course, the COVID pandemic, which forced consumers to transact digitally.
The pandemic pushed fintechs to become essential for business survival, acting as the main contributor of the rapid migration to digital payments.
So what is it about Israeli-founded fintech startups that stand out from their scaling neighbors across the pond? Israeli founders first and foremost have brought to the table a distinct perspective and understanding of where the gaps exist within their respective focus industries — whether it was Hippo and Lemonade in the world of property and casualty insurance, Rapyd and Melio in the world of business-to-business payments, or Earnix and Personetics in the world of banking data and analytics.
This is even more compelling given that many of these Israeli founders did not grow within financial services, but rather recognized those gaps, built their know-how around the industry (in some cases by hiring or partnering with industry experts and advisers during their ideation phase, strengthening their knowledge and validation), then sought to build more innovative and customer-focused solutions than most financial institutions can offer.
Having this in mind, it is becoming clearer that the Israeli fintech industry has slowly transitioned into a mature ecosystem with a combination of local talent, which now has expertise from a multitude of local fintechs that have scaled to success; a more global network of banking and insurance partners that have recognized the Israeli fintech disruptors; and the smart fintech -focused venture capital to go along with it. It’s a combination that will continue to set up Israeli fintech founders for success.
In addition, a major contributor to the fintech industry comes from the technological side. It is never enough to reach unicorn status with just the tech on the back end.
What most likely differentiates Israeli fintech from other ecosystems is the strong technological barriers and infrastructure built from the ground up, which then, of course, leads to the ability to be more customized, compliant, secured, etc. If I had to bet on where I believe Israeli fintech startups could become market leaders, I’d go with the following.
Voice technologies have come a long way over the years; where once you knew you were talking to a robot, now financial institutions and applications offer a fully automated experience that sounds and feels just like a company employee.
Israel has shown growing success in the world of voice tech, with companies like Gong.io providing insights for remote sales teams; Bonobo (acquired by Salesforce) offering insights from customer support calls, texts and other interactions; and Voca.ai (acquired by Snapchat) offering an automated support agent to replace the huge costs of maintaining call centers.
Finding the right learning platform can be difficult, especially as companies look to upskill and reskill their talent to meet demand for certain technological capabilities, like data science, machine learning and artificial intelligence roles.
Workera.ai’s approach is to personalize learning plans with targeted resources — both technical and nontechnical roles — based on the current level of a person’s proficiency, thereby closing the skills gap.
The Palo Alto-based company secured $16 million in Series A funding, led by New Enterprise Associates, and including existing investors Owl Ventures and AI Fund, as well as individual investors in the AI field like Richard Socher, Pieter Abbeel, Lake Dai and Mehran Sahami.
Kian Katanforoosh, Workera’s co-founder and CEO, says not every team is structured or feels supported in their learning journey, so the company comes at the solution from several angles with an assessment on mentorship, where the employee wants to go in their career and what skills they need for that, and then Workera will connect those dots from where the employee is in their skillset to where they want to go. Its library has more than 3,000 micro-skills and personalized learning plans.
“It is what we call precision upskilling,” he told TechCrunch. “The skills data then can go to the organization to determine who are the people that can work together best and have a complementary skill set.”
Workera was founded in 2020 by Katanforoosh and James Lee, COO, after working with Andrew Ng, Coursera co-founder and Workera’s chairman. When Lee first connected with Katanforoosh, he knew the company would be able to solve the problem around content and basic fundamentals of upskilling.
It raised a $5 million seed round last October to give the company a total of $21 million raised to date. This latest round was driven by the company’s go-to-market strategy and customer traction after having acquired over 30 customers in 12 countries.
Over the past few quarters, the company began working with Fortune 500 companies, including Accenture and Siemens Energy, across industries like professional services, medical devices and energy, Lee said. As spending on AI skills is expected to exceed $79 billion by 2022, he says Workera will assist in closing the gap.
“We are seeing a need to measure skills,” he added. “The size of the engagements are a sign as is the interest for tech and non-tech teams to develop AI literacy, which is a more pressing need.”
As a result, it was time to increase the engineering and science teams, Katanforoosh said. He plans to use the new funding to invest in more talent in those areas and to build out new products. In addition, there are a lot of natural language processes going on behind the scenes, and he wants the company to better understand it at a granular level so that the company can assess people more precisely.
Carmen Chang, general partner and head of Asia at NEA, said she is a limited partner in Ng’s AI fund and in Coursera, and has looked at a lot of his companies.
She said she is “very excited” to lead the round and about Workera’s concept. The company has a good understanding of the employee skill set, and with the tailored learning program, will be able to grow with company needs, Chang added.
“You can go out and hire anyone, but investing in the people that you have, educating and training them, will give you a look at the totality of your employees,” Chang said. “Workera is able to go in and test with AI and machine learning and map out the skill sets within a company so they will be able to know what they have, and that is valuable, especially in this environment.”
Connecting to all the services and microservices that a modern cloud native enterprise application requires can be a complicated task. It’s an area that startup Solo.io is trying to disrupt with the new release of its Gloo Mesh Enterprise platform.
Based in Cambridge, Massachusetts, Solo has had focus since its founding on a concept known as a service mesh. A service mesh provides an optimized approach to connect different components together in an automated approach, often inside of a Kubernetes cloud native environment.
Idit Levine, founder and CEO at Solo, explained to TechCrunch that she knew from the outset when she started the company in 2017 that it might take a few years till the market understood the concept of the service mesh and why it is needed. That’s why her company also built out an API gateway technology that helps developers connect APIs, which can be different data sources or services.
Until this week, the API and service mesh components of Solo’s Gloo Mesh Enterprise offering were separate technologies, with different configurations and control planes. That is now changing with the integration of both API and service mesh capabilities into a unified service. The integrated capabilities should make it easier to set up and configure all manner of services in the cloud that are running on Kubernetes.
Solo’s service mesh, known as Gloo Mesh, is based on the open source Istio project, which was created by Google. The API product is called Gloo Edge, which uses the open source Envoy project, originally created by ride sharing company Lyft. Levine explained that her team has now used Istio’s plugin architecture to connect with Envoy in an optimized approach.
Levine noted that many users start off with an API gateway and then extend to using the service mesh. With the new Gloo Mesh Enterprise update, she expects customer adoption to accelerate further as Solo will be able to differentiate against rivals in both the service mesh and API management markets.
While the service mesh space is still emerging including rivals such as Tetrate, API gateways are a more mature technology. There are a number of established vendors in the API management space including Kong which has raised $71 million in funding. Back in 2016, Google acquired API vendor Apigee for $625 million and has been expanding the technology in the years since, including the Apigee X platform announced in February of this year.
With the integration of Gloo Edge for API management into Gloo Mesh Enterprise, Solo isn’t quite covering all the bases for API technology, yet. Gloo Edge supports REST based APIs, which are by far the most common today, though it doesn’t support the emerging GraphQL API standard, which is becoming increasingly popular. Levine told us to ‘stay tuned’ for a future GraphQL announcement for Solo and its platform.
Solo has raised a total of $36.5 million across two rounds, with an $11 million Series A in 2018 and a $23 million Series B announced in October 2020. The company’s investors include Redpoint and True Ventures.
A London-headquartered startup called LOVE, valued at $17 million following its pre-seed funding, aims to redefine how people stay in touch with close family and friends. The company is launching a messaging app that offers a combination of video calling as well as asynchronous video and audio messaging, in an ad-free, privacy-focused experience with a number of bells and whistles, including artistic filters and real-time transcription and translation features.
But LOVE’s bigger differentiator may not be its product alone, but rather the company’s mission.
LOVE aims for its product direction to be guided by its user base in a democratic fashion as opposed to having the decisions made about its future determined by an elite few at the top of some corporate hierarchy. In addition, the company’s longer-term goal is ultimately to hand over ownership of the app and its governance to its users, the company says.
These concepts have emerged as part of bigger trends towards a sort of “Web 3.0,” or next phase of internet development, where services are decentralized, user privacy is elevated, data is protected and transactions take place on digital ledgers, like a blockchain, in a more distributed fashion.
LOVE’s founders are proponents of this new model, including serial entrepreneur Samantha Radocchia, who previously founded three companies and was an early advocate for the blockchain as the co-founder of Chronicled, an enterprise blockchain company focused on the pharmaceutical supply chain.
As someone who’s been interested in emerging technology since her days of writing her anthropology thesis on currency exchanges in “Second Life’s” virtual world, she’s now faculty at Singularity University, where she’s given talks about blockchain, AI, Internet of Things, Future of Work, and other topics. She’s also authored an introductory guide to the blockchain with her book “Bitcoin Pizza.”
Co-founder Christopher Schlaeffer, meanwhile, held a number of roles at Deutsche Telekom, including chief product & innovation officer, corporate development officer and chief strategy officer, where he along with Google execs introduced the first mobile phone to run Android. He was also chief digital officer at the telecommunication services company VEON.
The two crossed paths after Schlaeffer had already begun the work of organizing a team to bring LOVE to the public, which includes co-founders Chief Technologist Jim Reeves, also previously of VEON, and Chief Designer Timm Kekeritz, previously an interaction designer at international design firm IDEO in San Francisco, design director at IXDS and founder of design consultancy Raureif in Berlin, among other roles.
Image Credits: LOVE
Explained Radocchia, what attracted her to join as CEO was the potential to create a new company that upholds more positive values than what’s often seen today — in fact, the brand name “LOVE” is a reference to this aim. She was also interested in the potential to think through what she describes as “new business models that are not reliant on advertising or harvesting the data of our users,” she says.
To that end, LOVE plans to monetize without any advertising. While the company isn’t ready to explain its business model in full, it would involve users opting in to services through granular permissions and membership, we’re told.
“We believe our users will much rather be willing to pay for services they consciously use and grant permissions to in a given context than have their data used for an advertising model which is simply not transparent,” says Radocchia.
LOVE expects to share more about the model next year.
As for the LOVE app itself, it’s a fairly polished mobile messenger offering an interesting combination of features. Like any other video chat app, you can video call with friends and family, either in one-on-one calls or in groups. Currently, LOVE supports up to five call participants, but expects to expand that as it scales. The app also supports video and audio messaging for asynchronous conversations. There are already tools that offer this sort of functionality on the market, of course — like WhatsApp, with its support for audio messages, or video messenger Marco Polo. But they don’t offer quite the same expanded feature set.
Image Credits: LOVE
For starters, LOVE limits its video messages to 60 seconds, for brevity’s sake. (As anyone who’s used Marco Polo knows, videos can become a bit rambling, which makes it harder to catch up when you’re behind on group chats.) In addition, LOVE allows you to both watch the video content as well as read the real-time transcription of what’s being said — the latter which comes in handy not only for accessibility’s sake, but also for those times you want to hear someone’s messages but aren’t in a private place to listen or don’t have headphones. Conversations can also be translated into 50 languages.
“A lot of the traditional communication or messenger products are coming from a paradigm that has always been text-based,” explains Radocchia. “We’re approaching it completely differently. So while other platforms have a lot of the features that we do, I think that…the perspective that we’ve approached it has completely flipped it on its head,” she continues. “As opposed to bolting video messages on to a primarily text-based interface, [LOVE is] actually doing it in the opposite way and adding text as a sort of a magically transcribed add-on — and something that you never, hopefully, need to be typing out on your keyboard again,” she adds.
The app’s user interface, meanwhile, has been designed to encourage eye-to-eye contact with the speaker to make conversations feel more natural. It does this by way of design elements where bubbles float around as you’re speaking and the bubble with the current speaker grows to pull your focus away from looking at yourself. The company is also working with the curator of Serpentine Gallery in London, Hans Ulrich-Obrist, to create new filters that aren’t about beautification or gimmicks, but are instead focused on introducing a new form of visual expression that makes people feel more comfortable on camera.
For the time being, this has resulted in a filter that slightly abstracts your appearance, almost in the style of animation or some other form of visual arts.
The app claims to use end-to-end encryption and the automatic deletion of its content after seven days — except for messages you yourself recorded, if you’ve chosen to save them as “memorable moments.”
“One of our commitments is to privacy and the right-to-forget,” says Radocchia. “We don’t want to be or need to be storing any of this information.”
LOVE has been soft-launched on the App Store, where it’s been used with a number of testers and is working to organically grow its user base through an onboarding invite mechanism that asks users to invite at least three people to join. This same onboarding process also carefully explains why LOVE asks for permissions — like using speech recognition to create subtitles.
LOVE says its valuation is around $17 million USD following pre-seed investments from a combination of traditional startup investors and strategic angel investors across a variety of industries, including tech, film, media, TV and financial services. The company will raise a seed round this fall.
The app is currently available on iOS, but an Android version will arrive later in the year. (Note that LOVE does not currently support the iOS 15 beta software, where it has issues with speech transcription and in other areas. That should be resolved next week, following an app update now in the works.)
Earlier today, spend management startup Ramp said it has raised a $300 million Series C that valued it at $3.9 billion. It also said it was acquiring Buyer, a “negotiation-as-a-service” platform that it believes will help customers save money on purchases and SaaS products.
The round and deal were announced just a week after competitor Brex shared news of its own acquisition — the $50 million purchase of Israeli fintech startup Weav. That deal was made after Brex’s founders invested in Weav, which offers a “universal API for commerce platforms.”
From a high level, all of the recent deal-making in corporate cards and spend management shows that it’s not enough to just help companies track what employees are expensing these days. As the market matures and feature sets begin to converge, the players are seeking to differentiate themselves from the competition.
But the point of interest here is these deals can tell us where both companies think they can provide and extract the most value from the market.
These differences come atop another layer of divergence between the two companies: While Brex has instituted a paid software tier of its service, Ramp has not.
Let’s start with Ramp. Launched in 2019, the company is a relative newcomer in the spend management category. But by all accounts, it’s producing some impressive growth numbers. As our colleague Mary Ann Azevedo wrote:
Since the beginning of 2021, the company says it has seen its number of cardholders on its platform increase by 5x, with more than 2,000 businesses currently using Ramp as their “primary spend management solution.” The transaction volume on its corporate cards has tripled since April, when its last raise was announced. And, impressively, Ramp has seen its transaction volume increase year over year by 1,000%, according to CEO and co-founder Eric Glyman.
Ramp’s focus has always been on helping its customers save money: It touts a 1.5% cash back reward for all purchases made through its cards, and says its dashboard helps businesses identify duplicitous subscriptions and license redundancies. Ramp also alerts customers when they can save money on annual versus monthly subscriptions, which it says has led many customers to do away with established T&E platforms like Concur or Expensify.
All told, the company claims that the average customer saves 3.3% per year on expenses after switching to its platform — and all that is before it brings Buyer into the fold.
Chinese-backed and Africa-focused fintech company OPay raised $400 million in new financing led by SoftBank Vision Fund 2, Bloomberg reported Monday, valuing the company at $2 billion.
The round, which marks the fund’s first investment in an African startup, drew participation from existing investors like Sequoia Capital China, Redpoint China, Source Code Capital, and Softbank Ventures Asia. Other investors, including DragonBall Capital and 3W Capital, also took part in the new financing round.
This news comes three months after The Information reported that the company was in talks to raise “up to $400 million at a $1.5 billion valuation” from a group of Chinese investors. The new financing also comes two years after OPay announced two funding rounds in 2019 — $50 million in June and a $120 million Series B in November.
In an emailed statement, OPay CEO Yahui Zhou said OPay “wants to be the power that helps emerging markets reach a faster economic development.” The company, founded in 2018, had an exclusive presence in Nigeria before last year.
While the company started with providing customers with digital services in their everyday life from mobility and logistics to e-commerce and fintech at cheap rates, those super app plans have been largely underwhelming.
Right now, it’s the company’s mobile money and payment arm that thrives the most. By simply allowing unbanked and underbanked users in Nigeria to send and receive money and pay bills through a network of thousands of agents, OPay has grown at an exponential rate.
The company plays in an extremely competitive fintech market. Nigeria is Africa’s most populous nation, and with a large share of its people underbanked and unbanked, fintech is the most promising digital sector in the country. The same can be said for the continent as a whole. Mobile money services have long catered to the needs of the underbanked. Per GSMA, Africa had more than 160 million active mobile money users generating over $495 billion in transaction value last year.
Parent company Opera reported that OPay’s monthly transactions grew 4.5x to over $2 billion in December last year. OPay also claims to process about 80% of bank transfers among mobile money operators in Nigeria and 20% of the country’s non-merchant point of sales transactions. Last year, the company also said it acquired an international money transfer license with a WorldRemit partnership also in the works.
Per Bloomberg, the company’s monthly transaction volumes exceed $3 billion at the moment.
Last year, OPay expanded to Egypt, and according to the company, that’s an entry point to the Middle East market.
In a statement, Kentaro Matsui, a managing director at SoftBank Group Corp, said, “We believe our investment will help the company extend its offering to adjacent markets and replicate its successful business model in Egypt and other countries in the region.”
SoftBank joins a growing list of high-flying investors (Dragoneer, Sequoia, SVB Capital, among others) that have cut their first checks in African ventures this year. As the continent continues to show promise, fintech remains its poster child. This year up to half of the total investments raised have emerged from the sector; it contributed to more than 25% last year.
In addition, fintech has produced the most mega-rounds so far. TymeBank raised $109 million in February, Flutterwave bagged a $170 million round in March, while Chipper Cash secured $100 million in May.
OPay’s fundraise is the largest of the lot in terms of size and value, making it the second African fintech unicorn after Flutterwave and the third African unicorn after e-commerce giant Jumia. The three make up the five billion-dollar tech companies on the continent, which includes Interswitch and Fawry.
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:
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.
Advocates of algorithmic justice have begun to see their proverbial “days in court” with legal investigations of enterprises like UHG and Apple Card. The Apple Card case is a strong example of how current anti-discrimination laws fall short of the fast pace of scientific research in the emerging field of quantifiable fairness.
While it may be true that Apple and their underwriters were found innocent of fair lending violations, the ruling came with clear caveats that should be a warning sign to enterprises using machine learning within any regulated space. Unless executives begin to take algorithmic fairness more seriously, their days ahead will be full of legal challenges and reputational damage.
In late 2019, startup leader and social media celebrity David Heinemeier Hansson raised an important issue on Twitter, to much fanfare and applause. With almost 50,000 likes and retweets, he asked Apple and their underwriting partner, Goldman Sachs, to explain why he and his wife, who share the same financial ability, would be granted different credit limits. To many in the field of algorithmic fairness, it was a watershed moment to see the issues we advocate go mainstream, culminating in an inquiry from the NY Department of Financial Services (DFS).
At first glance, it may seem heartening to credit underwriters that the DFS concluded in March that Goldman’s underwriting algorithm did not violate the strict rules of financial access created in 1974 to protect women and minorities from lending discrimination. While disappointing to activists, this result was not surprising to those of us working closely with data teams in finance.
There are some algorithmic applications for financial institutions where the risks of experimentation far outweigh any benefit, and credit underwriting is one of them. We could have predicted that Goldman would be found innocent, because the laws for fairness in lending (if outdated) are clear and strictly enforced.
And yet, there is no doubt in my mind that the Goldman/Apple algorithm discriminates, along with every other credit scoring and underwriting algorithm on the market today. Nor do I doubt that these algorithms would fall apart if researchers were ever granted access to the models and data we would need to validate this claim. I know this because the NY DFS partially released its methodology for vetting the Goldman algorithm, and as you might expect, their audit fell far short of the standards held by modern algorithm auditors today.
In order to prove the Apple algorithm was “fair,” DFS considered first whether Goldman had used “prohibited characteristics” of potential applicants like gender or marital status. This one was easy for Goldman to pass — they don’t include race, gender or marital status as an input to the model. However, we’ve known for years now that some model features can act as “proxies” for protected classes.
If you’re Black, a woman and pregnant, for instance, your likelihood of obtaining credit may be lower than the average of the outcomes among each overarching protected category.
The DFS methodology, based on 50 years of legal precedent, failed to mention whether they considered this question, but we can guess that they did not. Because if they had, they’d have quickly found that credit score is so tightly correlated to race that some states are considering banning its use for casualty insurance. Proxy features have only stepped into the research spotlight recently, giving us our first example of how science has outpaced regulation.
In the absence of protected features, DFS then looked for credit profiles that were similar in content but belonged to people of different protected classes. In a certain imprecise sense, they sought to find out what would happen to the credit decision were we to “flip” the gender on the application. Would a female version of the male applicant receive the same treatment?
Intuitively, this seems like one way to define “fair.” And it is — in the field of machine learning fairness, there is a concept called a “flip test” and it is one of many measures of a concept called “individual fairness,” which is exactly what it sounds like. I asked Patrick Hall, principal scientist at bnh.ai, a leading boutique AI law firm, about the analysis most common in investigating fair lending cases. Referring to the methods DFS used to audit Apple Card, he called it basic regression, or “a 1970s version of the flip test,” bringing us example number two of our insufficient laws.
Ever since Solon Barocas’ seminal paper “Big Data’s Disparate Impact” in 2016, researchers have been hard at work to define core philosophical concepts into mathematical terms. Several conferences have sprung into existence, with new fairness tracks emerging at the most notable AI events. The field is in a period of hypergrowth, where the law has as of yet failed to keep pace. But just like what happened to the cybersecurity industry, this legal reprieve won’t last forever.
Perhaps we can forgive DFS for its softball audit given that the laws governing fair lending are born of the civil rights movement and have not evolved much in the 50-plus years since inception. The legal precedents were set long before machine learning fairness research really took off. If DFS had been appropriately equipped to deal with the challenge of evaluating the fairness of the Apple Card, they would have used the robust vocabulary for algorithmic assessment that’s blossomed over the last five years.
The DFS report, for instance, makes no mention of measuring “equalized odds,” a notorious line of inquiry first made famous in 2018 by Joy Buolamwini, Timnit Gebru and Deb Raji. Their “Gender Shades” paper proved that facial recognition algorithms guess wrong on dark female faces more often than they do on subjects with lighter skin, and this reasoning holds true for many applications of prediction beyond computer vision alone.
Equalized odds would ask of Apple’s algorithm: Just how often does it predict creditworthiness correctly? How often does it guess wrong? Are there disparities in these error rates among people of different genders, races or disability status? According to Hall, these measurements are important, but simply too new to have been fully codified into the legal system.
If it turns out that Goldman regularly underestimates female applicants in the real world, or assigns interest rates that are higher than Black applicants truly deserve, it’s easy to see how this would harm these underserved populations at national scale.
Modern auditors know that the methods dictated by legal precedent fail to catch nuances in fairness for intersectional combinations within minority categories — a problem that’s exacerbated by the complexity of machine learning models. If you’re Black, a woman and pregnant, for instance, your likelihood of obtaining credit may be lower than the average of the outcomes among each overarching protected category.
These underrepresented groups may never benefit from a holistic audit of the system without special attention paid to their uniqueness, given that the sample size of minorities is by definition a smaller number in the set. This is why modern auditors prefer “fairness through awareness” approaches that allow us to measure results with explicit knowledge of the demographics of the individuals in each group.
But there’s a Catch-22. In financial services and other highly regulated fields, auditors often can’t use “fairness through awareness,” because they may be prevented from collecting sensitive information from the start. The goal of this legal constraint was to prevent lenders from discrimination. In a cruel twist of fate, this gives cover to algorithmic discrimination, giving us our third example of legal insufficiency.
The fact that we can’t collect this information hamstrings our ability to find out how models treat underserved groups. Without it, we might never prove what we know to be true in practice — full-time moms, for instance, will reliably have thinner credit files, because they don’t execute every credit-based purchase under both spousal names. Minority groups may be far more likely to be gig workers, tipped employees or participate in cash-based industries, leading to commonalities among their income profiles that prove less common for the majority.
Importantly, these differences on the applicants’ credit files do not necessarily translate to true financial responsibility or creditworthiness. If it’s your goal to predict creditworthiness accurately, you’d want to know where the method (e.g., a credit score) breaks down.
In Apple’s example, it’s worth mentioning a hopeful epilogue to the story where Apple made a consequential update to their credit policy to combat the discrimination that is protected by our antiquated laws. In Apple CEO Tim Cook’s announcement, he was quick to highlight a “lack of fairness in the way the industry [calculates] credit scores.”
Their new policy allows spouses or parents to combine credit files such that the weaker credit file can benefit from the stronger. It’s a great example of a company thinking ahead to steps that may actually reduce the discrimination that exists structurally in our world. In updating their policies, Apple got ahead of the regulation that may come as a result of this inquiry.
This is a strategic advantage for Apple, because NY DFS made exhaustive mention of the insufficiency of current laws governing this space, meaning updates to regulation may be nearer than many think. To quote Superintendent of Financial Services Linda A. Lacewell: “The use of credit scoring in its current form and laws and regulations barring discrimination in lending are in need of strengthening and modernization.” In my own experience working with regulators, this is something today’s authorities are very keen to explore.
I have no doubt that American regulators are working to improve the laws that govern AI, taking advantage of this robust vocabulary for equality in automation and math. The Federal Reserve, OCC, CFPB, FTC and Congress are all eager to address algorithmic discrimination, even if their pace is slow.
In the meantime, we have every reason to believe that algorithmic discrimination is rampant, largely because the industry has also been slow to adopt the language of academia that the last few years have brought. Little excuse remains for enterprises failing to take advantage of this new field of fairness, and to root out the predictive discrimination that is in some ways guaranteed. And the EU agrees, with draft laws that apply specifically to AI that are set to be adopted some time in the next two years.
The field of machine learning fairness has matured quickly, with new techniques discovered every year and myriad tools to help. The field is only now reaching a point where this can be prescribed with some degree of automation. Standards bodies have stepped in to provide guidance to lower the frequency and severity of these issues, even if American law is slow to adopt.
Because whether discrimination by algorithm is intentional, it is illegal. So, anyone using advanced analytics for applications relating to healthcare, housing, hiring, financial services, education or government are likely breaking these laws without knowing it.
Until clearer regulatory guidance becomes available for the myriad applications of AI in sensitive situations, the industry is on its own to figure out which definitions of fairness are best.
Disney’s streaming service is seeing improved growth, after initially seeing slower numbers of subscriber additions in Q2 as Covid lockdowns and mask mandates came to an end. Today, Disney+ beat analyst expectations for subscriber growth in Disney’s blowout third quarter, reaching 116 million paid subscribers — above the 114.5 million Wall Street had expected — and up over 100% year-over-year.
Disney also topped expectations across the board, with $17.02 billion in revenue versus the $16.76 billion expected, and earnings per share of 80 cents, above analysts’ expectations of 55 cents. Even Disney Parks were back in business.
The pandemic had thrown a wrench in forecasting growth metrics across a number of industries, streaming included. Although Disney+ has well-established itself as one of the few competitors capable of challenging Netflix in an increasingly crowded market, it has seen some ups and downs due to Covid impacts. In the earlier days of the pandemic, streaming was on the rise. This March, Disney+ passed 100 million subscribers after just 16 months of operation. At the time, Disney execs said the service was on track to meet its projections of 260 million subscribers by 2024.
But in Disney’s second quarter earnings, the economy’s re-opening impacted Disney+ numbers, as people finally had more to do than just sit at home, and vaccinations become more widely available. Then, Disney+ only reached 103.6 million subscribers, when analysts were expecting 109.3 million, and the stock slipped as a result.
Disney wasn’t alone in feeling the impacts of Covid-induced lumpiness in subscriber additions. Netflix had also seen slower subscriber growth earlier in the year due to Covid and its far-reaching effects on things like production delays and release schedules.
But Netflix’s most recent quarter, where it once again topped subscriber estimates, had hinted that Disney+ may see a similar boost. Aiding in that growth, was Disney+’s recent market expansions in Asia. Disney+ Hotstar, arrived in Malaysia and Thailand in June, after prior launches in India and Indonesia last year.
The Hotstar version of Disney+, however, led to lowered average monthly revenue per user (ARPU) in the quarter due to its lower price points. In Q3, ARPU declined from $4.62 to $4.16 due to a higher mix of Disney+ Hotstar subscribers compared with the prior-year quarter, Disney said.
Disney’s other streaming services, Hulu and ESPN+, didn’t see the same trend.
Hulu’s subscription video service jumped from $11.39 to $13.15 year-over-year and its Live TV service (+SVOD) grew from $68.11 to $84.09. ESPN+ also grew from $4.18 to $4.47.
Subscriber growth also increased across the services, with ESPN+ growing 75% year-over-year to reach 14.9 million customers and total Hulu subscribers growing 21% to reach 42.8 million.
“…Our direct-to-consumer business is performing very well, with a total of nearly 174 million subscriptions across Disney+, ESPN+ and Hulu at the end of the quarter, and a host of new content coming to the platform,” noted Disney CEO Bob Chapek in a press release.
Across Disney’s direct-to-consumer business, revenues grew 57% to $4.3 billion and its operating loss declined from $0.6 billion to $0.3 billion, thanks to improved results from Hulu, including subscription growth and higher ad revenues.
These gains were offset by a higher loss at Disney+ attributed to programming, production, marketing and technology costs that were somewhat mitigated by increases in subscription revenues and success of the Disney+ Premier Access release of “Cruella.” (Disney’s fiscal quarter ended July 3, so the impacts of the massive haul that “Black Widow” saw following its U.S. opening — nor the resulting lawsuit from star Scarlett Johansson, for that matter — have yet to be included in these figures.)
This narrative oversimplifies the evolution that’s happening in the financial services sector. Storing and moving money and extending credit in a regulated environment is difficult. And differentiating your offering from incumbent financial institutions requires much more than superficial tweaks.
What really makes a fintech company extends far beyond user interface enhancements and delivering financial services to end customers. It’s what’s “under the hood” — the full-stack approach that allows fintech companies to truly innovate for their customers.
What really makes a fintech company extends far beyond user interface enhancements and delivering financial services to end customers.
Embedded finance helps companies and brands outside of the core financial sector distribute financial services. This requires varying levels of effort from the company and looks like anything from Starbucks offering an integrated wallet and payments within its app to Lyft offering a debit card to their drivers. But that doesn’t make Starbucks or Lyft fintech companies.
The “every company will be a fintech” stance investors are bullish on conflates multiple approaches to inlaying financial offerings, coupling the resurgence of white-labeled financial services (which have been around for decades) with the rising banking, payments and lending-as-a-service players. The latter approach allows companies to customize their financial product experience while outsourcing many core financial services tasks. The former is simply distribution through embedded delivery.
There are four core tenets to fully operate as a financial services provider: a customer-facing product, transactional infrastructure, risk management and compliance, and customer servicing. In the case of lending, there is a fifth tenet: Companies also need to be able to manage capital. Embedded financial services help companies sidestep the majority of what it really means to be a fintech.
While embedded finance is hot today, white-labeled financial services have been around for decades. Branded credit cards, for example, are a common paradigm for white-labeling. They quickly became a lasting way to incentivize consumer loyalty but don’t signal real effort or know-how in financial services. United and Alaska don’t run credit checks, configure billing or handle disputes for cardholding customers, nor do they assume any risk by embossing their logo on a card. The partnerships are major money makers for airlines while the risk stays on the financial institutions’ side (Chase, Bank of America and Visa). This risk can even account for significant loss on the financial side: According to American Express, 21% of its outstanding credit card loans belonged to people with a Delta credit card a few years ago.
This white-labeling approach is becoming common for other services, coming to life in forms like banking offerings from cell carriers, and it’s by design: Financial services are complex and highly regulated, so brands prefer to defer most of the work to the experts. So while United, Delta or T-Mobile offer financial services under their brand, they are definitely not becoming fintech companies.
In contrast, some corporations are seeing the opportunity to build financial services from the ground up. Walmart’s move to snag Goldman Sachs talent to lead its foray into finance (with Ribbit at the helm) shows promise for a true fintech spinout.
The investment in expertise in compliance and risk management furthers the company’s potential to build detailed and relevant infrastructure from the get-go — a significant step beyond the retailer’s many existing white-labeled financial partnerships.
Tools and turnkey solutions that help non-finance companies build financial applications more recently came into the mix: VCs are enthusiastic about new players building embedded payments, lending and, more recently, banking platform services (also known as BaaS) through APIs and backend tools.
As opposed to financial infrastructure services provided directly by sponsor banks or processors providing payments or ledger services, these platforms abstract the underlying infrastructure, wrap them with friendly-to-use APIs, and bundle core financial elements like risk management, compliance and servicing. While these platforms do offer some self-efficacy for companies to provide financial services, their major limitation is that they’re general purpose by design.
Fintechs found an opportunity to serve customers overlooked and underserved by traditional finance through specialization. Traditional financial institutions long applied the generalist model, carrying hundreds of SKUs and serving all segments. This strategy inevitably led banks to invest more in services for their most profitable customers, optimizing for their needs. Less profitable segments were left with stale and one-size-fits-all offerings.
Fintechs’ success with these underserved segments is derived from a relentless pursuit and laser focus on addressing core customers’ unique needs, building products and services designed for them. In order to deliver on this promise, fintechs must innovate across all layers of the stack — from the product experience and feature set to the infrastructure and risk management, all the way down to servicing.
UI is not nearly enough to differentiate, and addressing customers’ needs while minding overall unit economics is critical. One fintech’s choices on these matters may be completely different from another if they address different segments — it all boils down to tradeoffs. For example, deciding on which data sources to use and balancing between onboarding and transactional risk look different if optimizing for freelancers rather than larger small businesses.
In contrast, third-party platform providers must be generic enough to power a broad range of companies and to enable multiple use cases. While the companies partnering with these services can build and customize at the product feature level, they are heavily reliant on their platform partner for infrastructure and core financial services, thus limited to that partner’s configurations and capabilities.
As such, embedded platform services work well to power straightforward commoditized tasks like credit card processing, but limit companies’ ability to differentiate on more complex offerings, like banking, which require end-to-end optimization.
More generally and from a customer’s perspective, embedded fintech partnerships are most effective when providing confined financial services within specific user flows to enhance the overall user experience.
For example, a company can offer credit at the point of sale through a third-party provider to enable a purchase. However, when considering general purpose and standalone financial services, the benefits of embedded fintech are much weaker.
The biggest proponents of embedded finance argue that large companies and brands can be successful with finance add-ons on their platforms because of their brand recognition and install base.
But that overlooks the reality of choice in the market: Just because a customer does one facet of their business with a company doesn’t necessarily mean they want that company as their provider for everything, especially if the service is inferior to what they can get elsewhere.
While the fintech market booms and legacy brands continue to buy into the opportunity, verticalized, full-stack fintechs will trump their generic offerings time and time again. Some aspects of embedded finance and white-labeling will continue to crop up or prevail, like payment processing and buy now, pay later services. But customers will continue to choose the banks/neobanks, lenders and tools built for them and their own unique needs, bucking the “every company is a fintech” fallacy.
I worked at Google for six years. Internally, you have no choice — you must use Kubernetes if you are deploying microservices and containers (it’s actually not called Kubernetes inside of Google; it’s called Borg). But what was once solely an internal project at Google has since been open-sourced and has become one of the most talked about technologies in software development and operations.
For good reason. One person with a laptop can now accomplish what used to take a large team of engineers. At times, Kubernetes can feel like a superpower, but with all of the benefits of scalability and agility comes immense complexity. The truth is, very few software developers truly understand how Kubernetes works under the hood.
I like to use the analogy of a watch. From the user’s perspective, it’s very straightforward until it breaks. To actually fix a broken watch requires expertise most people simply do not have — and I promise you, Kubernetes is much more complex than your watch.
How are most teams solving this problem? The truth is, many of them aren’t. They often adopt Kubernetes as part of their digital transformation only to find out it’s much more complex than they expected. Then they have to hire more engineers and experts to manage it, which in a way defeats its purpose.
Where you see containers, you see Kubernetes to help with orchestration. According to Datadog’s most recent report about container adoption, nearly 90% of all containers are orchestrated.
All of this means there is a great opportunity for DevOps startups to come in and address the different pain points within the Kubernetes ecosystem. This technology isn’t going anywhere, so any platform or tooling that helps make it more secure, simple to use and easy to troubleshoot will be well appreciated by the software development community.
In that sense, there’s never been a better time for VCs to invest in this ecosystem. It’s my belief that Kubernetes is becoming the new Linux: 96.4% of the top million web servers’ operating systems are Linux. Similarly, Kubernetes is trending to become the de facto operating system for modern, cloud-native applications. It is already the most popular open-source project within the Cloud Native Computing Foundation (CNCF), with 91% of respondents using it — a steady increase from 78% in 2019 and 58% in 2018.
While the technology is proven and adoption is skyrocketing, there are still some fundamental challenges that will undoubtedly be solved by third-party solutions. Let’s go deeper and look at five reasons why we’ll see a surge of startups in this space.
Docker revolutionized how developers build and ship applications. Container technology has made it easier to move applications and workloads between clouds. It also provides as much resource isolation as a traditional hypervisor, but with considerable opportunities to improve agility, efficiency and speed.