December 10, 2018
Category archive

Balderton Capital

Contentful raises $33.5M for its headless CMS platform

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Contentful, a Berlin- and San Francisco-based startup that provides content management infrastructure for companies like Spotify, Nike, Lyft and others, today announced that it has raised a $33.5 million Series D funding round led by Sapphire Ventures, with participation from OMERS Ventures and Salesforce Ventures, as well as existing investors General Catalyst, Benchmark, Balderton Capital and Hercules. In total, the company has now raised $78.3 million.

It’s only been less than a year since the company raised its Series C round and as Contentful co-founder and CEO Sascha Konietzke told me, the company didn’t really need to raise right now. “We had just raised our last round about a year ago. We still had plenty of cash in our bank account and we didn’t need to raise as of now,” said Konietzke. “But we saw a lot of economic uncertainty, so we thought it might be a good moment in time to recharge. And at the same time, we already had some interesting conversations ongoing with Sapphire [formeraly SAP Ventures] and Salesforce. So we saw the opportunity to add more funding and also start getting into a tight relationship with both of these players.”

The original plan for Contentful was to focus almost explicitly on mobile. As it turns out, though, the company’s customers also wanted to use the service to handle its web-based applications and these days, Contentful happily supports both. “What we’re seeing is that everything is becoming an application,” he told me. “We started with native mobile application, but even the websites nowadays are often an application.”

In its early days, Contentful also focuses only on developers. Now, however, that’s changing and having these connections to large enterprise players like SAP and Salesforce surely isn’t going to hurt the company as it looks to bring on larger enterprise accounts.

Currently, the company’s focus is very much on Europe and North America, which account for about 80% of its customers. For now, Contentful plans to continue to focus on these regions, though it obviously supports customers anywhere in the world.

Contentful only exists as a hosted platform. As of now, the company doesn’t have any plans for offering a self-hosted version, though Konietzke noted that he does occasionally get requests for this.

What the company is planning to do in the near future, though, is to enable more integrations with existing enterprise tools. “Customers are asking for deeper integrations into their enterprise stack,” Konietzke said. “And that’s what we’re beginning to focus on and where we’re building a lot of capabilities around that.” In addition, support for GraphQL and an expanded rich text editing experience is coming up. The company also recently launched a new editing experience.

News Source = techcrunch.com

The war over music copyrights

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VC firms haven’t been the only ones raising hundreds of millions of dollars to invest in a booming market. After 15+ years of being the last industry anyone wanted to invest in, the music industry is coming back, and money is flooding in to buy up the rights to popular songs.

As paid streaming subscriptions get mainstream adoption, the big music streaming services – namely Spotify, Apple Music, and Tencent Music, but also Pandora, Amazon Music, YouTube Music, Deezer, and others – have entered their prime. There are now over 51 million paid subscription accounts among music streaming services in the US. The music industry grew 8% last year globally to $17.3 billion, driven by a 41% increase in streaming revenue and 45% increase in paid streaming revenue.

The surge in music streaming means a surge in income for those who own the copyrights to songs, and the growth of entertainment in emerging markets, growing use in digital videos, and potential use of music in new content formats like VR only expand this further. Unsurprisingly, private equity firms, family offices, corporates, and pension funds want a piece of the action.

There are two general types of copyrights for a song: the publishing rights and the master rights. The musical composition of a song – the lyrics, melodies, etc. – comes from songwriters who own the publishing right (though generally they sign a publishing deal and their publisher gets ownership of it in addition to half the royalties). Meanwhile, the version of a song being performed comes from the recording artist who owns the master right (though usually they sign a record deal and the record label gets ownership of the masters and most of the royalties).

Popular songs are valuable to own because of all the royalties they collect: whenever the song is played on a streaming service, downloaded from iTunes, or covered on YouTube (a mechanical license), played over radio or in a grocery store (a performance license), played as soundtrack over a movie or TV show (a sync license), and for other uses. More royalty income from a song goes to the master owner since they took on more financial risk marketing it, but publishers collect royalties from some channels that master owners don’t (like radio play, for instance).

For a songwriter behind popular songs, these royalties form a predictable revenue stream that can amount to tens of thousands, hundreds of thousands, or even millions of dollars per year. Of course, most songs that are written or recorded don’t make any money: creating a track that breaks out in a crowded industry is hard. This scarcity – there are only so many thousands of popular musicians and a limited number of legendary artists whose music stays relevant for decades – means copyrights for successful musicians command a premium when they or their publisher decide to sell them.

Investing in streaming economics

In 2017, revenue from streaming services accounted for 38% of worldwide music industry revenue, finally overtaking revenue from traditional album sales and song downloads. Subscription streaming services hit a pivot point in gaining mainstream adoption, but they still have far to go. Goldman Sachs media sector analyst Lisa Yang predicted that by 2030, the global music industry will reach $41 billion in market size as the global streaming market multiplies in size to $34 billion (nearly all of it from paid subscriptions).

Merck Mercuriadis is seen on the left. (Photo by KMazur/WireImage for Conde Nast media group)

Earlier this week, I spoke with Merck Mercuriadis who has managed icons like Elton John, Guns N’ Roses, and Beyoncé and raised £200 million ($260 million) on the London Stock Exchange in June for an investment vehicle (Hipgnosis Songs) to acquire the catalogues of top songwriters. His plan is to raise and invest £1 billion over the next three to five years, arguing that the shift to passive consumers paying for music will take the industry to heights it has never seen before.

Indeed, streaming music is a paradigm shift from the past. With all the world’s music available in one interface for free (with ads) or for an affordable subscription (without ads), consumers no longer have to actively choose which specific songs to buy (or even which to download illegally).

With it all in front of them and all included in the price, people are listening to a broader range of music: they’re exploring more genres, discovering more musicians who aren’t stars on traditional radio, and going back to music from past decades. Consumers who weren’t previously buying a lot of music are now subscribing for $120 per year and spreading it across more artists.

Retail businesses are doing the same: through streaming offerings like Soundtrack Your Brand (which spun out of Spotify), they’re using commercial licenses – which are more expensive – to stream a broader array of music in stores rather than putting on the radio or playing the same few CDs.

Much of the music industry’s market growth is happening in China, India, Latin America, and emerging markets like Nigeria where subscription apps are replacing widespread music piracy or non-consumption. Tencent Music Entertainment, whose three streaming services have roughly 75% market share in China (a music market that expanded by 34% last year), is preparing for an IPO that could give it roughly the same $29 billion valuation Spotify received in its IPO in April. Meanwhile, music industry revenue from Latin America grew 18% last year.

Western music is infused in pop culture worldwide, so as these countries enter the streaming era they are monetizing hundreds of millions of additional listeners, through ad revenue at a minimum but increasingly through paid subscriptions as well.

At the talent management, publishing, and production firm Primary Wave, founder Larry Mestel is seeing emerging markets drive more revenue to his clients (like Smokey Robinson, Alice Cooper, Melissa Etheridge, and the estate of Bob Marley) as new fan bases engage with their music online. He raised a new $300 million fund (backed by Blackrock and other institutions) in 2016 to acquire rights in music catalogues amid a market he says has improved substantially due to growth opportunities stemming from the streaming model.

It’s not just streaming music platforms that are driving growth either. Streaming video has exploded, whether it’s from short YouTube videos or the growing number of shows on platforms like Hulu and Amazon Prime Video, and with that comes growing sync licensing of songs for their soundtracks; global sync licensing revenue was up 10% year-over-year in 2017 alone. Over the last year, Facebook signed licenses with every large publisher to cover use of song clips by its users in Instagram Stories and Facebook videos as well.

The inflating valuations of songs catalogues

Catalogues are commonly valued based on the “net publisher’s share,” which is the average amount of annual royalty money left over after paying out any percentages owed to others (like a partial stake in the royalties still held by the artist).

When Round Hill Music acquired Carlin for $245 million in January to gain ownership in the catalogues of Elvis Presley, James Brown, AC/DC, and others, it paid a 16x multiple on net publisher share, which is high but not uncommon in the current market when trading catalogues of legendary artists. Just three years ago, multiples anchored in the 10-12 range (or less for newer or smaller artists whose music has not yet shown the same longevity).

Avid Larizadeh Duggan left her role as a general partner at GV to become Chief Strategy & Business Officer of Kobalt

Kobalt, which raised $205 million from VC firms like GV and Balderton Capital to become a technology-centric publisher and label services powerhouse, has also become an active player in the space. Aside from its core operating business (where it stands out from traditional publishers and labels for not taking control of clients’ copyrights), it has raised two funds ($600M for the most recent one) to help institutional investors like the Railpen pension fund in the UK gain exposure to music copyrights as an asset class. In December, their fund acquired the catalogue of publisher SONGS Music Publishing for a reported $160M in a sale process against 13 other bidders looking to buy ownership in songs by Lorde, The Weeknd, and other young pop and hip-hop artists.

Too high a price?

The natural question to ask when there’s a rapid surge of money (and a corresponding surge in prices) in an asset class is whether there’s a bubble. After all, last year’s industry revenues were still only 68% of those in 1999 and the rate of growth will inevitably slow once streaming has captured the early majority of consumers.

But the fundamentals driving this capital are in line with a secular shift – it’s evident that music streaming still has a lot of room to grow in a few short years, especially as a large portion of the human population is just coming online (and doing so over mobile first). Plus as new content formats like augmented and virtual reality come to fruition, new categories of music sync licensing will inevitably accompany them for their soundtracks.

Each catalogue is its own case, of course. As Shamrock Capital managing director Jason Sklar emphasized to me, the rising tide isn’t lifting all boats equally. The streaming revolution appears to be disproportionately benefiting hip-hop, rap, and pop given the youth skew of streaming service users and the digital-native social media engagement of the artists in those genres.

Beyond the purchase price, the critical variable for evaluating a deal in this market is also the operational value a potential buyer can provide to the catalogue: their ability to actively promote songs from the past by pitching them to new TV shows, ad campaigns, and any number of other projects that will keep them culturally relevant. This is where strategic investors have an advantage over purely financial investors in publishing rights, especially when it comes to the longer tail of middle-tier artist’s whose music doesn’t naturally get the inbound demand that the Beatles or Prince catalogues do.

With strong long-term market growth and a wide range of possible niches and strategies, music copyrights are an asset class where we’ll see a number of major new players develop.

News Source = techcrunch.com

Luno raises $9M to bring its bitcoin wallet, exchange and services to Europe

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Luno, a bitcoin wallet and exchange based out of Singapore, is riding the crypto wave into Europe after it closed a $9 million Series B round for market expansion.

The funding was led by new investor Balderton Capital, with participation from existing backer Digital Currency Group. South Africa’s AlphaCode — also a new arrival on the cap table — joined the deal which takes Luno, which was formerly called BitX, to $13.8 million raised to date.

Major league investor Naspers, another that hails from South Africa, led a $4 million investment in June 2015.

The products these backers are throwing their weight behind include a bitcoin wallet for storing crypto currencies, an exchange for buying them and merchant services that enable banks and retailers to work with bitcoin. In South Africa, in particular, Luno has worked with the likes of Pick N Pay while it was among the first batch let into the FCA’s Regulatory Sandbox in London last year.

Luno said the money will go towards bringing those services to 35 new countries in Europe. The company — which has offices in Singapore, Cape Town and London — plans to double its current headcount of 70 staff to support this new sprint, which takes its services to a total of 40 countries worldwide.

“[The expansion] might sound quite trivial but as you probably know there are not a lot of companies that offer these kind of services in Europe — certainly not in a very mass-market, user-friendly way, and particularly with a really good mobile product coupled with good customer service,” Luno CEO Marcus Swanepoel told TechCrunch.

“As we expand the team and grow in these countries we will be rolling out more deposit methods and country localization that we are already working on,” he added.

Bitcoin has been a tear this year, with the crypto currency’s value against the U.S. dollar reaching new highs in 2017. It reached $2,000 for the first time in May before surging to $3,000 and then $4,000 in August. Bitcoin touched $5,000 on some exchanges earlier this month before a ban on trading in China, and other market uncertainties saw the price decline to around $4,000 as of today.

Despite that volatility, companies and investors see the potential for the digital currency particularly around cross border transfers and — with the bitcoin blockchain — infrastructure and operational opportunities for the banking industry. Ethereum, the world’s second most popular crypto coin, is also emerging as a platform for developers, beyond helping companies raise money via ICOs.

Featured Image: BTC Keychain/Flickr UNDER A CC BY 2.0 LICENSE

News Source = techcrunch.com

Balderton joins $30M Series D for big data biotech platform play, Sophia Genetics

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Switzerland based SaaS startup Sophia Genetics is hoping to give IBM Watson a run for its money in the healthcare diagnostics space. It’s built a big data analytics platform that harnesses clinicians’ medical expertise to enhance genomic diagnostic via AI algorithms — leading, it says, to better and faster diagnoses for patients with diseases such as cancer.

Hospitals that use the platform are intended to jointly benefit from expert-fed, algorithmic DNA sequencing diagnostic insights exactly because they are shared across the platform. So as the user-base scales — it says it’s adding 10 new hospitals each month — Sophia Genetics’ AIs get smarter and more accurate, and patients anywhere can benefit from the pooled knowledge.

The company is announcing a $30 million Series D funding round today, adding UK-based VC firm Balderton Capital to its investor roster, along with 360 Capital Partners. Previous investors including UK tech entrepreneur Mike Lynch’s Invoke Capital, and Alychlo, started by Mark Coucke, a Belgian pharmaceutical entrepreneur.

According to Crunchbase the biotech business has raised $28.75M since being founded back in 2011, so has pulled in in the region of $58.75M thus far — capital that’s been used to develop its platform proposition to a tipping point of utility, as co-founder and CEO Dr Jurgi Camblong explains.

As the cost of genome sequencing has come down he says the challenge for healthcare providers has been quickly and accurately reading and analyzing more readily available DNA sequencing data. This is where Sophia Genetics’ analytics platform aims to assist — currently targeting oncology, hereditary cancer, metabolic disorders, pediatrics and cardiology.

“With the decreasing costs of these technologies that [are] basically digitalizing patients’ DNA information, we did see an opportunity to engage with hospitals to help them be part of a community and share experience and knowledge to continuously better diagnose and treat patients through the use of such type of digital technologies,” he tells TechCrunch.

“Since our dream was to impact on better diagnosing of the maximum number of patients we thought that in the end the best way was helping every hospital to leverage on this genomic technology. Rather than build a company that would end up competing with the hospitals. And so that’s why we built a software as a service platform.”

However, for the platform play to work Camblong says the company needed to be able to attract hospitals to sign up even before it had algorithms that could offer accelerated diagnostic insights — so it needed to be able to offer them something of value right away to get them involved.

And while Camblong said the team’s initial thought was that processing and storage would likely be the major challenges for hospitals handling what are extremely large genomic data-sets, along with issues such as data integrity, privacy and visualization, they actually found the main problem hospitals were grappling with was data accuracy. So they set out to help with that to offer early utility and win longer term buy in from clinicians.

“All of them [were] purchasing those technologies to basically better diagnose patients but the data they would produce, although they would be larger, would not be as accurate as what they would have with legacy technology — and this is where we were somehow forced as a startup…  to develop algorithms that would correct the data so that clinicians would be able to rely on this data. And use this data to better diagnose patients,” he says.

“This is really how we started, from 2011 where we had nothing, to launching our platform in 2014 where we were 20 employees and we were working with I think 50 hospitals by the end of 2014. To today where we are working with over 350 hospitals that are all connected through our SaaS platform, who are all pulling patients’ genome data, sharing knowledge to continuously get a better outcome of our algorithms that by the time [i.e. now] have become an artificial intelligence.”

On the data accuracy issue, Camblong says the startup worked with hospitals to benchmark DNA samples analyzed via their sequencing systems, with the aim of “getting the signal out of the noise”, as he puts it, and then training algorithms of its own to be able to perform that de-noising process automatically, and to recognize the salient/relevant patterns in the genome data. And thus, ultimately, to speed up diagnoses in the targeted health areas.

Sophia Genetics refers to its business as sitting within the “fast-emerging field of data-driven medicine” — and is specifically applying AI to enhance relatively modern, so-called “Next Generation DNA Sequencing” (NGS) methods, which may be faster than but aren’t as accurate as older-gen legacy systems, according to Camblong.

“All the AI technology that we’ve developed is based on statistical inference, pattern recognition, and some of it as well on machine learning,” he says of Sophia Genetics’ core tech.

Data are not valuable any more once you have them. In any AI industry what is interesting is seeing the capacity to be exposed to the problem and teach an algorithm on how to recognize and solve the problem.

“Data are not valuable any more once you have them,” he adds, fleshing out the startup’s relationship with its hospital customers/partners. “In any AI industry what is interesting is seeing the capacity to be exposed to the problem and teach an algorithm on how to recognize and solve the problem. But once you have taught this AI [to do] that you don’t need any more the data you’ve been computing. So it’s not so much the fact that we get access to this data — it’s because, unlike any other actor in the industry, we took this challenge of taking the pain.

“Unlike no other company we understood that the problem was accuracy and we took the challenge of aggregating the problem of accuracy.”

Commenting on why Sophia Genetics stood out for Balderton, partner James Wise told us: “On top of their easy to use workflow tool to annotate and use sequenced data (compared with unsupported open source software) and their active clinician community, Sophia’s real technological advantage comes out of its machine learning technology that analyses the genomic data and minimizes the noise from the use of multiple different combinations of sequencers and diagnostic kits to identify variants (DNA alterations) with a clinical-grade accuracy.”

“As the market for diagnostic kits continues to expand, and as new sequencers come to market, there will continue to be a plethora of different ways that clinicians can use genomic data to make a diagnosis. But this requires a sophisticated third party platform to handle these many different inputs and to optimize their outcomes — in Sophia Genetics’ case by using machine learning techniques across the huge datasets and through testing with their clinician network,” he added.

“While there are competing solutions for tertiary analysis that may work well with a certain type of sequencer, it is Sophia’s independent position and its technical ability to incorporate any combination of diagnostic and sequencer that makes its technology universal and unique.”

Camblong says Sophia Genetics has benchmarked DNA sequencing data for more than 10,000 patients, and for over 500,000 unique variants at this stage — and currently has three “core” diagnostic technologies trained off of this data.

It says the process it uses has been validated with more than 340 different DNA sequencers, while its algorithms were built bottom-up from raw FASTQ data (aka the most common file format used in DNA sequencing) — and claims its tech is universally applicable.

“You cannot use deep learning techniques in this industry,” says Camblong, elaborating on why the business took several years to train algorithms manually, with human experts benchmarking and analyzing data. “You need to have the prior knowledge. Deep learning requires you to have millions of millions of millions of data. And then you can expect that because of that eventually the neurons you will build are going to be able to find the way by their own. In many industries you need to have prior knowledge.

“First for the accuracy phase, Sophia has been learning by our data scientists because they have been exposed to the patterns [i.e. by analyzing the DNA sequencing data]… and then at the second stage, once you have a platform… the platform can evolve and learn with machine learning techniques.”

At this stage he says the business is in its second phase — utilizing the network of hospitals and clinicians it has signed up and linked via its platform, and drawing on the access to thousands of cases it’s been afforded, coupled with the continued elbow grease of clinicians feeding their diagnostic knowledge on the pathogenicity of variants into the platform on an ongoing basis — to be in a position to now apply machine learning techniques to accelerate utility and scale the business. Hence taking in more funding.

Camblong refers to what the platform does as a “democratization” of DNA sequencing expertise, asserting: “So that the next hospital that starts using your technology will enter at a level where it will require less competencies, less experience to be able to diagnose patients through the use of genomic information.”

It charges hospitals for use of the platform on an on-demand basis — so they pay per analysis performed, rather than having to shell out for a fixed monthly fee.

The workflow for using the platform involves a patient with one of the suspected conditions arriving at the hospital and having a sample taken. Their DNA is extracted and enriched with molecular biology principles, and genes selected to be redone by the hospital’s NGS machine.

The digitization of that data takes two days, after which users log in to Sophia Genetics’ platform and load in the raw data, which is transferred to the company’s datacenters (“in an anonymized way”, according to Camblong; he also confirms that the platform prompts hospitals to confirm it has patients’ consent for transferring their data to be processed by a third party) — and then the startup’s AI algorithms get to work to pull out unique genetic variants.

“These data are going to be annotated… it means that you add additional information that is out there in public databases, or as well in the databases of the users of Sophia DDM, and then the data are being ranked according to pathogenicity predictions,” he continues, noting that the data processing undertaken by its AI takes two hours.

“Two hours later the user logs in and given the genetic variants that are being detected the user is going to take action — so Sophia can learn as well from these actions. The expert is going to classify those variants as being pathogenic or benign.”

Camblong says the platform has moved from having a precision rate of 85% for classification of variants for the first 10,000 patients, to 95% with the following 10,000, and 98% with the 10,000 after that.

“We are always between 99.9% and 100% for sensitivity, and between 99% and 100% specificity,” he adds of the platform’s current average accuracy range.

As it evolves, he says the wider vision is to add more layers to expand its capabilities — so it could, for example, compute imaging data from medical scans together with molecular genomics data to support more powerful predictive analyses.

“If you combine two sequence images and molecular information about [a cancer] tumor you can predict how the tumor is going to evolve in the following months,” he suggests, saying surgeons could then make decisions about whether they need to operate immediately or whether they could wait. So the big push is towards the opportunity of an ever more personalized form of healthcare — enabled by AI being able to shrink the time-scales and costs of performing robust genomic analysis.

He says the new funding will be used to “fully deploy” Sophia’s SaaS platform globally, and to ramp up commercial activity — moving beyond its current focus on Europe to Latin America, AsiaPac, Canada and the U.S.

“We believe that the number of hospitals that will adopt our technology will dramatically ramp up over the next year,” he says.

The investment will also go into oncology, specifically — towards developing what he calls “full management of a cancer case”, explaining this as encompassing: “From the first image that has been taken with a scan, up to the monitoring of the efficiency of the treatment and eventually adaptation of the treatment.”

It also intends to add additional capacity generally, so it can associate molecular information with metadata, such as imaging data — to start to push towards expanding the platform’s analytical capabilities by supporting the co-processing of multiple types of healthcare data pertaining to its targeted conditions.

Though Camblong concedes that the privacy challenges will step up as more highly sensitive medical data gets processed in concert.

“We took [privacy] very serious. There are companies in the industry that have made very bad moves in the past. And we have never wanted to go to a DTC [direct to consumer] approach. For us it was very clear that if you wanted to impact on better diagnosing the maximum number of patients, trust by the institutions would be very important,” he says.

“You cannot roll out an AI unless you build it bottom up. So everything you’ve been challenging me about on how we’ve been able to build this AI to make it accurate is really what distinguishes Sophia from any other actor that may want to be important in this space. We have been the only one who made the effort of digging into this complexity of making those data accurate — and of making everything bottom up, because that’s the only way you can build smart intelligence, or artificial intelligence,” he adds.

“To take a parallel, self-driving cars are not going to learn from speech recognition systems — they will learn from you, from me, from people that are going to drive cars, make mistakes, take right decisions and by knowing whether we have taken the right decision or whether we’ve made mistakes we are going to be able to teach the cars how to drive themselves.”

News Source = techcrunch.com

Virtual science lab startup Labster bags $10M to accelerate its ed tech play

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Ed tech startup Labster whose software platform enables virtual simulations of laboratories for teaching life science to students, has closed a $10 million Series A round of funding led by early stage European VC firm Balderton Capital. Stockholm-based Northzone is also joining the round, as is Unity Technologies founder David Helgason — clearly spying strategic potential in a platform that makes use of 3D gaming environments for an educational purpose.

Labster launched its lab simulation software back in 2013, after founding the business in 2011 and initially working on the concept in stealth. It has created around 65 simulations thus far, covering life science study topics — from a basic introduction to acids and bases where students perform a simulation of handling corrosive chemicals and get to see the consequences of not following good lab safety protocol, to a simulation of using a confocal microscope (a piece of lab kit that can run to multiple thousands of dollars in its physical form).

All the 3D simulations include games and challenges designed to keep students engaged and learning — such as murder mystery puzzles and multiple choice quiz questions, with text theory also available for students’ reference. The 3D environments are designed much like point and click adventure games, meaning lab equipment can be interacted with and environments navigated by clicking around.

The startup’s grand vision is to replace the role of textbooks for science education up to graduate level with a more interactive learning experience enabled by virtual simulations of lab equipment and experiments — so a scalable and accessible gamification of the learning experience, which does not require an institution to shell out on expensive, real-world lab equipment in order for its students to learn.

Still, CEO and co-founder Mads Bonde concedes universities aren’t the faster adopters of new technologies, so he describes the learning “status quo” — i.e. textbooks and lectures — as Labster’s main competitor at this point.

“We know the way that the education space is moving it’s not going to happen overnight, but even the university sector publishers know that that’s the way that things are going — that the next version of what is now called the text book will be much more interactive,” he tells TechCrunch. “In the medium term it definitely needs to co-exist with a lot of other methods — including books, including videos, including other areas.”

Rethinking education inside a 3D simulation

When we last spoke to Labster, in 2013 for the launch of its product, it had raised $1M in grants and non-equity support at that point. Bonde tells us now it has since topped up that to a further ~$10M — which is in addition to the now closed $10M Series A VC investment.

“We’ve been in dialogue with a lot of investors over the years but have chosen to focus on a combination of things, so bootstrapping but especially sales early on combined with grants from especially the government here in Europe and in Denmark,” he says. “That has funded our collaborations with universities… which is kind of like the hidden way that we’ve gotten to where we are today.”

He says the reason Labster is taking VC funding now is as a result of being approached by an undisclosed strategic investor who was keen to collaborate — which pushed it to take a closer look at the options of either continuing to grow organically vs taking VC cash and putting their foot on the gas to accelerate product development and expansion in multiple markets.

“We ended up concluding that VC funding was the right thing for us to do at this stage because we had scaled our product and now really need to fuel sales, marketing and getting our product across globally — so that fueled the decision there,” says Bonde, confirming this particular strategic investor was not Unity’s Helgason.

Labster’s general calculus is that, ultimately, it will cost the same to develop 3D educational content via its platform as it costs to create content for a textbook. Although Bonde concedes there’s still “a huge imbalance” in the cost. But one way it’s trying to shrink that differential more quickly is via a content builder system it’s created atop its simulation platform — enabling users to build out its content proposition.

Some of Labster’s Series A funding will be going towards this “lab builder” system — which lets educators and individuals create bespoke simulator content for other students to learn.

The majority of content currently available on the platform has been developed by Labster working with educational institute customers — including some high profile institutions, such as the Massachusetts Institute of Technology in the US and Imperial College in London — but that presents an obvious speed bump to scaling the business so you can see why they’re looking for ways to widen the content creation pipe.

“We have students who have done this as well,” notes Bonde of lab builder created content. “That’s also our long term vision to revolutionize the way that science is taught by anyone being able to create high class 3D simulations like games and deploy them through our platform.”

“If you hit a core part of the curriculum, each simulation should last for a very long time,” adds Balderton’s Sam Myer, who heads up the Nordics region for the investment firm, on the content pricing point — noting also that Labster’s simulations can be reused “over and over”.

“The cost actually isn’t that different,” he argues.

Myer says Labster’s proposition stood out for the VC firm on account of it being “at the center of the education market as a whole going digital”, and for the way it’s “rethinking what you can do when you have digital platforms to build on”. — i.e. rather than trying to bring existing formats online; be that textbooks (to ebooks) or lectures (online streams) or indeed whole courses (MOOCs).

“Labster is really rethinking what educational content should look like when you can design it from the ground up using these new tools,” he tells TechCrunch. “So it’s a very different approach to ed tech that many other companies have.”

Currently Labster has around 150 institutions globally using its platform, with the UK and the US as its primary markets. (Bonde also flags users in Asian markets, including in Hong Kong, Singapore, and in South America.)

It has a blended business model, where it sells b2b to universities and educational institutions as well as b2c to students who might want to make individual use of its tools to aid their learning — and it’s intended to invest to expand on both fronts with the new funding.

Expanding simulation content across more science topics is also on the roadmap for the next 12 months. Plus, it will be expanding sales and marketing efforts, especially targeting universities in the US and the UK, with Bonde noting “heavy investment” there” — including growing the headcount of the team.

“Over the last five years we’ve really focused on developing this platform which can handle educational simulations but with the game engine built on top of Unity so it’s possible to design learning experiences where you can learn the way that we believe humans are supposed to learn,” he argues. “The way that children learn as well — which is through interacting, through talking with different people, through experiencing the way the world is. And the you internalize that and learn from that.”

And while guided simulations might at least suggest the potential for an even wider disruption of traditional teaching institutions, say by removing the need for such institutions to exist at all, Bonde says there’s still a key role for universities to play — in hosting content and ensuring quality is up to a benchmarked educational standard.

Myer agrees. “I think universities are always going to be important in terms of guiding what should be taught, and how these simulations should be designed — and so it’s always going to be important to partner with some of the universities that [Labster is] already doing work with,” he says. “The leaders in this space… And then each university’s main differentiation, their main brand is what they teach — and how good their education is, and their sort of stamp of approval. So each university will want to have a say in what gets taught. So I think they’ll always be important.”

Virtual reality as a key ed tech — just not yet

Another component of Labster’s vision is VR and AR. Virtual reality and augmented reality are areas of special interest for the startup, although — pragmatically and some might say realistically — it’s not built its platform to be reliant on these technologies. Rather it’s taking a cross-platform approach, meaning users of laptops and tablets can make use of the platform so there’s no requirement for any dedicated headset hardware. This allows it to address the current reality of the kit students and universities own, even as it contemplates transformational VR-enabled learning environments becoming a mass market future reality.

Bonde is certainly convinced that immersive VR simulations will play a leading role in the future of education, noting for example that the technology is developing to support feedback via additional peripherals such as tactile gloves. He also believes augmented reality will play a key bridging role — helping students who have cut their undergraduate teeth learning in entirely virtual 3D labs to transition to using real-world lab equipment for post-grad learning (where purely 3D simulations would not suffice).

But his conviction is tempered with patience that such changes will only come over the longer term. “We believe that in the long term virtual reality and AR as well will be one of the  main ways to learn. But it’s not going to happen overnight,” he suggests. “So what we’ve done is to create a platform where it’s possible to use it on a computer and on VR with the same code-base and the same simulation basically. So the students can choose which device. And they can even switch between devices — both VR and non-VR.”

“Right now VR is not crucial for our proposition,” he adds. “With laptops etc we get the big leap forward… The way that you interact in a game-like environment — that’s really where you get the major benefit.”

Balderton’s Myer is also cool on VR in the near to medium term. “VR, down the line, I think will be the best way to display what they can do,” he says of Labster’s lab simulations. “Labster’s really about learning by doing. And they have these very strong 3D simulations. Right now the [VR] hardware — the actual devices — aren’t available to most universities or companies or schools that want to be using it. And so it’s important to be able to do these 3D simulations on your mobile phone, or on your laptop or whatever you might be using.

“But they’re building it to have this very rich 3D environment that can be translated to VR. So I think VR is going to change the way that education happens in a pretty big way… There’s a lot of research around simulations and how they really improve learning outcomes. And then VR is a really good format for these simulations.”

Asked how bullish he is about VR generally, Myer suggests the tech still has a long road ahead of it to achieve mainstream adoption — though he sees nearer term applications for education use-cases vs gaming. “I think it’s going to take a while before we see the mass market. Especially when we talk about things like casual gaming — it’s going to take a while,” he argues.

“I’ve spent quite a lot of time looking at gaming as well, where VR is seen as one of the platform they’re trying to build for. And actually the use-case for education at the moment is much stronger than gaming. If you think of what you do when you’re trying to learn in general is you pick up a textbook or whatever and you try to submerge yourself into this body of content for a long period of time. And so VR, given that it’s a very submersive experience, is actually very challenging to do for gaming where you’re sort of dipping in and out… It’s actually very difficult to build a game that works well for VR at this stage.”

In developing its learning platform, Bonde says Labster has worked with psychologists to try identify educational advantages that can be enabled by technology, such as tracking users’ emotions and blood pressure and analyzing brain scans during use of the platform — and based on what it’s seen from this research he claims VR advances the learning experience because users are “totally immersed”.

But he simultaneously emphasizes Labster’s tech agnosticism, adding: “It’s important for us not to be reliant on virtual reality propagation for our success.”

News Source = techcrunch.com

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