It was only five years ago that electronic punk band YACHT entered the recording studio with a daunting task: they would train an AI on fourteen years of their music, then synthesize the results into the album “Chain Tripping.”
“I’m not interested in being a reactionary,” YACHT member and tech writer Claire L. Evans said in a documentary about the album. “I don’t want to return to my roots and play acoustic guitar because I’m so freaked out about the coming robot apocalypse, but I also don’t want to jump into the trenches and welcome our new robot overlords either.”
But our new robot overlords are making a whole lot of progress in the space of AI music generation. Even though the Grammy-nominated “Chain Tripping” was released in 2019, the technology behind it is already becoming outdated. Now, the startup behind the open source AI image generator Stable Diffusion is pushing us forward again with its next act: making music.
Harmonai is an organization with financial backing from Stability AI, the London-based startup behind Stable Diffusion. In late September, Harmonai released Dance Diffusion, an algorithm and set of tools that can generate clips of music by training on hundreds of hours of existing songs.
“I started my work on audio diffusion around the same time as I started working with Stability AI,” Zach Evans, who heads development of Dance Diffusion, told TechCrunch in an email interview. “I was brought on to the company due to my development work with [the image-generating algorithm] Disco Diffusion and I quickly decided to pivot to audio research. To facilitate my own learning and research, and make a community that focuses on audio AI, I started Harmonai.”
Dance Diffusion remains in the testing stages — at present, the system can only generate clips a few seconds long. But the early results provide a tantalizing glimpse at what could be the future of music creation, while at the same time raising questions about the potential impact on artists.
The emergence of Dance Diffusion comes several years after OpenAI, the San Francisco-based lab behind DALL-E 2, detailed its grand experiment with music generation, dubbed Jukebox. Given a genre, artist and a snippet of lyrics, Jukebox could generate relatively coherent music complete with vocals. But the songs Jukebox produced lacked larger musical structures like choruses that repeat, and often contained nonsense lyrics.
Google’s AudioLM, detailed for the first time earlier this week, shows more promise, with an uncanny ability to generate piano music given a short snippet of playing. But it hasn’t been open sourced.
Dance Diffusion aims to overcome the limitations of previous open source tools by borrowing technology from image generators such as Stable Diffusion. The system is what’s known as a diffusion model, which generates new data (e.g., songs) by learning how to destroy and recover many existing samples of data. As it’s fed the existing samples — say, the entire Smashing Pumpkins discography — the model gets better at recovering all the data it had previously destroyed to create new works.
Kyle Worrall, a Ph.D. student at the University of York in the U.K. studying the musical applications of machine learning, explained the nuances of diffusion systems in an interview with TechCrunch:
“In the training of a diffusion model, training data such as the MAESTRO data set of piano performances is ‘destroyed’ and ‘recovered,’ and the model improves at performing these tasks as it works its way through the training data,” he said via email. “Eventually the trained model can take noise and turn that into music similar to the training data (i.e., piano performances in MAESTRO’s case). Users can then use the trained model to do one of three tasks: Generate new audio, regenerate existing audio that the user chooses, or interpolate between two input tracks.”
It’s not the most intuitive idea. But as DALL-E 2, Stable Diffusion and other such systems have shown, the results can be remarkably realistic.
For example, check out this Disco Diffusion model fine-tuned on Daft Punk music:
Or this style transfer of the Pirates of the Caribbean theme to flute:
Or this style transfer of Smash Mouth vocals to the Tetris theme (yes, really):
Or these models, which were fine-tuned on copyright-free dance music:
Jona Bechtolt of YACHT was impressed by what Dance Diffusion can create.
“Our initial reaction was like, ‘Okay, this is a leap forward from where we were at before with raw audio,’” Bechtolt told TechCrunch.
Unlike popular image-generating systems, Dance Diffusion is somewhat limited in what it can create — at least for the time being. While it can be fine-tuned on a particular artist, genre or even instrument, the system isn’t as general as Jukebox. The handful of Dance Diffusion models available — a hodgepodge from Harmonai and early adopters on the official Discord server, including models fine-tuned with clips from Billy Joel, The Beatles, Daft Punk and musician Jonathan Mann’s Song A Day project — stay within their respective lanes. That is to say, the Jonathan Mann model always generates songs in Mann’s musical style.
And Dance Diffusion-generated music won’t fool anyone today. While the system can “style transfer” songs by applying the style of one artist to a song by another, essentially creating covers, it can’t generate clips longer than a few seconds in length and lyrics that aren’t gibberish (see the below clip). That’s the result of technical hurdles Harmonai has yet to overcome, says Nicolas Martel, a self-taught game developer and member of the Harmonai Discord.
“The model is only trained on short 1.5-second samples at a time so it can’t learn or reason about long-term structure,” Martel told TechCrunch. “The authors seem to be saying this isn’t a problem, but in my experience — and logically anyway — that hasn’t been very true.”
YACHT’s Evans and Bechtolt are concerned about the ethical implications of AI – they are working artists, after all – but they observe that these “style transfers” are already part of the natural creative process.
“That’s something that artists are already doing in the studio in a much more informal and sloppy way,” Evans said. “You sit down to write a song and you’re like, I want a Fall bass line and a B-52’s melody, and I want it to sound like it came from London in 1977.”
But Evans isn’t interested in writing the dark, post-punk rendition of “Love Shack.” Rather, she thinks that interesting music comes from experimentation in the studio – even if you take inspiration from the B-52’s, your final product may not bear the signs of those influences.
“In trying to achieve that, you fail,” Evans told TechCrunch. “One of the things that attracted us to machine learning tools and AI art was the ways in which it was failing, because these models aren’t perfect. They’re just guessing at what we want.”
Evans describes artists as “the ultimate beta testers,” using tools outside of the ways in which they were intended to create something new.
“Oftentimes, the output can be really weird and damaged and upsetting, or it can sound really strange and novel, and that failure is delightful,” Evans said.
Assuming Dance Diffusion one day reaches the point where it can generate coherent whole songs, it seems inevitable that major ethical and legal issues will come to the fore. They already have, albeit around simpler AI systems. In 2020, Jay-Z ‘s record label filed copyright strikes against a YouTube channel, Vocal Synthesis, for using AI to create Jay-Z covers of songs like Billy Joel’s “We Didn’t Start the Fire.” After initially removing the videos, YouTube reinstated them, finding the takedown requests were “incomplete.” But deepfaked music still stands on murky legal ground.
Perhaps anticipating legal challenges, OpenAI for its part open-sourced Jukebox under a non-commercial license, prohibiting users from selling any music created with the system.
“There is little work into establishing how original the output of generative algorithms are, so the use of generative music in advertisements and other projects still runs the risk of accidentally infringing on copyright, and as such damaging the property,” Worrall said. “This area needs to be further researched.”
An academic paper authored by Eric Sunray, now a legal intern at the Music Publishers Association, argues that AI music generators like Dance Diffusion violate music copyright by creating “tapestries of coherent audio from the works they ingest in training, thereby infringing the United States Copyright Act’s reproduction right.” Following the release of Jukebox, critics have also questioned whether training AI models on copyrighted musical material constitutes fair use. Similar concerns have been raised around the training data used in image-, code-, and text-generating AI systems, which is often scraped from the web without creators’ knowledge.
Technologists like Mat Dryhurst and Holly Herndon founded Spawning AI, a set of AI tools built for artists, by artists. One of their projects, “Have I Been Trained,” allows users to search for their artwork and see if it has been incorporated into an AI training set without their consent.
“We are showing people what exists within popular datasets used to train AI image systems, and are initially offering them tools to opt out or opt in to training,” Herndon told TechCrunch via email. “We are also talking to many of the biggest research organizations to convince them that consensual data is beneficial for everyone.”
But these standards are — and will likely remain — voluntary. Harmonai hasn’t said whether it’ll adopt them.
“To be clear, Dance Diffusion is not a product and it is currently only research,” said Zach Evans of Stability AI. “All of the models that are officially being released as part of Dance Diffusion are trained on public domain data, Creative Commons-licensed data, and data contributed by artists in the community. The method here is opt-in only and we look forward to working with artists to scale up our data through further opt-in contributions, and I applaud the work of Holly Herndon and Mat Dryhurst and their new Spawning organization.”
YACHT’s Evans and Bechtolt see parallels between the emergence of AI generated art and other new technologies.
“It’s especially frustrating when we see the same patterns play out across all disciplines,” Evans told TechCrunch. “We’ve seen the way that people being lazy about security and privacy on social media can lead to harassment. When tools and platforms are designed by people who aren’t thinking about the long term consequences and social effects of their work like that, things happen.”
Jonathan Mann — the same Mann whose music was used to train one of the early Dance Diffusion models — told TechCrunch that he has mixed feelings about generative AI systems. While he believes that Harmonai has been “thoughtful” about the data they’re using for training, others like OpenAI have not been as conscientius.
“Jukebox was trained on thousands of artists without their permission — it’s staggering,” Mann said. “It feels weird to use Jukebox knowing that a lot of folks’ music was used without their permission. We are in uncharted territory.”
From a user perspective, Waxy’s Andy Baio speculates in a blog post that new music generated by an AI system would be considered a derivative work, in which case only the original elements would be protected by copyright. Of course, it’s unclear what might be considered “original” in such music. Using this music commercially is to enter uncharted waters. It’s a simpler matter if generated music is used for purposes protected under fair use, like parody and commentary, but Baio expects that courts would have to make case-by-base judgements.
According to Herndon, copyright law is not structured to adequately regulate AI art-making. Evans also points out that the music industry has been historically more litigious than the visual art world, which is perhaps why Dance Diffusion was explicitly trained on a dataset of copyright-free or voluntarily-submitted material, while DALL-E mini will easily spit out a Pikachu if you input the term “Pokémon.”
“I have no illusion that that’s because they thought that was the best thing to do ethically,” Evans said. “It’s because copyright law in music is very strict and more aggressively enforced.”
Gordon Tuomikoski, an arts major at the University of Nebraska-Lincoln who moderates the official Stable Diffusion Discord community, believes that Dance Diffusion has immense artistic potential. He notes that some members of the Harmonai server have created models trained on dubstep “webs,” kicks and snare drums and backup vocals, which they’ve strung together into original songs.
“As a musician, I definitely see myself using something like Dance Diffusion for samples and loops,” Tuomikoski told TechCrunch via email.
Martel sees Dance Diffusion one day replacing VSTs, the digital standard used to connect synthesizers and effect plugins with recording systems and audio editing software. For example, he says, a model trained on ’70s jazz rock and Canterbury music will intelligently introduce new “textures” in the drums, like subtle drum rolls and “ghost notes,” in the same way that artists like John Marshall might — but without the manual engineering work normally required.
Take this Dance Diffusion model of Senegalese drumming, for instance:
And this model of snares:
And this model of a male choir singing in the key of D across three octaves:
And this model of Mann’s songs fine-tuned with royalty-free dance music:
“Normally, you’d have to lay down notes in a MIDI file and sound-design really hard. Achieving a humanized sound this way is not only very time-consuming, but requires a deeply intimate understanding of the instrument you’re sound designing,” Martel said. “With Dance Diffusion, I look forward to feeding the finest ’70s prog rock into AI, an infinite unending orchestra of virtuoso musicians playing Pink Floyd, Soft Machine and Genesis, trillions of new albums in different styles, remixed in new ways by injecting some Aphex Twin and Vaporwave, all performing at the peak of human creativity — all in collaboration with your own personal tastes.”
Mann has greater ambitions. He’s currently using a combination of Jukebox and Dance Diffusion to play around with music generation, and plans to release a tool that’ll allow others to do the same. But he hopes to one day use Dance Diffusion — possibly in conjunction with other systems — to create a “digital version” of himself capable of continuing the Song A Day project after he passes away.
“The exact form it’ll take hasn’t quite become clear yet … [but] thanks to folks at Harmonai and some others I’ve met in the Jukebox Discord, over the last few months I feel like we’ve made bigger strides than any time in the last four years,” Mann said. “I have over 5,000 Song A Day songs, complete with their lyrics as well as rich metadata, with attributes ranging from mood, genre, tempo, key, all the way to location and beard (whether or not I had a beard when I wrote the song). My hope is that given all this data, we can create a model that can reliably create new songs as if I had written them myself. A Song A Day, but forever.”
If AI can successfully make new music, where does that leave musicians?
YACHT’s Evans and Bechtolt point out that new technology has upended the art scene before, and the results weren’t as catastrophic as expected. In the 1980s, the UK Musicians Union attempted to ban the use of synthesizers, arguing that it would replace musicians and put them out of work.
“With synthesizers, a lot of artists took this new thing and instead of refusing it, they invented techno, hip hop, post punk and new wave music,” Evans said. “It’s just that right now, the upheavals are happening so quickly that we don’t have time to digest and absorb the impact of these tools and make sense of them.”
Still, YACHT worries that AI could eventually challenge work that musicians do in their day jobs, like writing scores for commercials. But like Herndon, they don’t think AI can quite replicate the creative process just yet.
“It is divisive and a fundamental misunderstanding of the function of art to think that AI tools are going to replace the importance of human expression,” Herndon said. “I hope that automated systems will raise important questions about how little we as a society have valued art and journalism on the internet. Rather than speculate about replacement narratives, I prefer to think about this as a fresh opportunity to revalue humans.”
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Privilège Ventures launches $20M fund investing in women-led startups • TechCrunch
Lugano, Switzerland-based venture capital fund Privilège Ventures just launched its fourth fund. The CHF 20 million (just over $20 million) fund is earmarked for women-led early-stage startups across Europe.
“We don’t just want to support women,” Jacqueline Ruedin Rüsch, founding general partner at Privilège Ventures said in an interview with TechCrunch. “The data shows women in the driver’s seat produce better ROI.”
The firm says that its investment thesis is based on the statistical evidence that women perform better than men in leadership roles.
“The numbers are staggering. It’s not just about being ethical and doing good: global GDP would grow 6% if rates of entrepreneurship were equal between men and women,” said Lucian Wagner, Privilège Ventures founding general partner in a press statement.
The firm’s thesis is backed up by research from Boston Consulting Group on investment and revenue data over a five-year period. The study also showed that startups founded and co-founded by women received less than half the average investments made into companies led by men, even though the female led startups generated 10% more revenue over time.
“There are very few funds worldwide dedicated to backing female founders, and despite the rapid growth in the VC industry the percentage of female or gender-diverse-led teams is falling,” said Rüsch. “I started my professional life in the banking sector in Switzerland: this was, and partially still is, a very male-driven sector. I became used to being one of the few females in big conference rooms and I didn’t even pay any more attention to it. But when I got pregnant the first reaction from my senior colleagues was, ‘When will you stop working?’ This was quite shocking, I must admit.”
As Alex reported back in July, PitchBook data suggests that the percentage of venture capital deals that included at least one woman founder fell from 19.4% to 18.2%. In Europe, the numbers are even more dire. Privilège suggests that in Europe, female founders receive barely 1% of total VC investments.
Privilège Ventures’ LPs are mainly high net-worth individuals and family offices, the firm says, and the fund aims to write 15-20 early-stage checks, with initial investments in the $250,000 range.
“I really like to invest in founders at the very beginning of their journey. Often we meet them even before they have incorporated their company and we track them, coach them and see how they take their first steps in the entrepreneurial journey. Given our focus in seed stage, we feel it is key to be as close as possible with our companies and for this reason we have a preference for our local market, Switzerland, and the surrounding European countries,” Rüsch explains. “We are not specialized in a specific sector but we have some preferences, namely in medtech, deep tech and in general for the digital economy. We like to enter as soon as possible, even pre-seed, and are happy to continue investing in the best companies up to Series A.”
The firm says it would love to see more companies trying to solve “real” problems — solutions that can save lives, preserve the planet and products that are not just “nice to have” but are “must-have.”
“Our overall portfolio already counts over 30% of companies with a female co-founder. As we aim to invest only in top-performing teams, we need to guarantee a strong deal flow and for this reason, we will look not only to Switzerland but to Europe as well with a higher focus on certain countries such as Italy, France and Germany, being closer to us,” says Rüsch, explaining why investing specifically in women continues to make sense for the fund. “Some will point to the simple fact that having different viewpoints in the room leads to more thoughtful decision-making — some will point to women having battled through a lot of hassles to get where they are. We see firsthand that women are driven to tackle problems that have been overlooked in tech — but can have a profound impact on the world. We already have startups in our portfolio with female founders or leaders working on using neurotech to improve sleep, fungicides to improve food and biomarkers to continually measure proteins and hormones to prevent and monitor health conditions, just to name a few.”
Former Googlers raise more than $90M to scale alternative asset fintech startup • TechCrunch
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Hellooooo, guess what? It’s November! We guess it was actually November yesterday, too, but we failed to notice, because LOL what even is time, amirite. Anyway, put away your Halloween costumes and start the game of How Long Can You Avoid “Little Drummer Boy”? If you do want to play that game, you’d be well advised to not click this link, although that’s a particularly tolerable version of the song, to be fair.
The TechCrunch Top 3
- And for his next act…: Manish was on a roll again today, covering some cool stories. The first is on some former Googlers rallying around their peer Caesar Sengupta, who raised $90 million to scale Arta Finance, a company that will provide individuals similar access to alternative assets that are usually reserved for the ultrawealthy.
- Betting on web3: Manish’s second story is on Microsoft, which is backing South Korea–based web3 game developer Wemade.
- Come together, right now, in the cloud: Though many companies are asking employees to come back into the office, they and others are still figuring out how to keep distributed teams working as one. Former Yext CEO Howard Lerman thinks he has created the best option with Roam, a company that came out of stealth today with $30 million in new funding, Kyle reports.
Startups and VC
New data from more than 200 startups show that CTOs earn higher salaries than their CEO counterparts. Mostly, co-founders make the same, but where there is a difference, the balance typically tips in the favor of the technical co-founder, Haje reports.
Also, we’ve got an eclectic mix of additional news for ya:
Dear Sophie: How can students work or launch a startup while maintaining their immigration status?
I’m studying bioinformatics at a university in the U.S.
What options do I have to work before and after graduation on my student visa? Do any of these options allow me to launch my own startup?
— Wanting to Work
Three more from the TC+ team:
Big Tech Inc.
Elon Musk met with civil rights leaders, and Amanda has all the details on what went down. Many of the leaders were concerned with content moderation, particularly dealing with increases in hate speech and undue influence on the midterm elections. Meanwhile, Natasha M writes that another Twitter executive is reportedly flying the coop.
And we have five more for you:
Alation bags $123M at a $1.7B valuation for its data-cataloging software • TechCrunch
There’s been an explosion of enterprise data in recent years, accelerated by pandemic-spurred digital transformations. An IDC report commissioned by Seagate projected companies would collect 42.2% more data by year-end 2022 than in 2020, amounting to multiple petabytes of data in total. While more data is generally a good thing, particularly where it concerns analytics, large volumes can be overwhelming to organize and govern — even for the savviest of organizations.
That’s why Satyen Sangani, a former Oracle VP, co-founded Redwood City–based Alation, a startup that helps crawl a company’s databases in order to build data search catalogs. After growing its customer base to over 450 brands and annual recurring revenue (ARR) to over $100 million, Alation has raised $123 million in a Series E round led by Thoma Bravo, Sanabil Investments, and Costanoa Ventures with participation from Databricks Ventures, Dell Technologies Capital, Hewlett Packard Enterprise, Icon Ventures, Queensland Investment Corporation, Riverwood Capital, Salesforce Ventures, Sapphire Ventures and Union Grove, the company announced today.
The all-equity tranche values Alation at over $1.7 billion — an impressive 15 times higher than the company’s previous valuation in a challenging economic climate. In an interview with TechCrunch, Sangani said the new capital — which brings Alation’s total raised to $340 million — will be put toward investments in product development (including through acquisitions) and expanding Alation’s sales, engineering and marketing teams, with a focus on the public sector and corporations based in Asia Pacific, Europe, Latin America and the Middle East.
“With the capital, we will continue to focus on engagement and adoption, collaboration, governance, lineage, and on APIs and SDKs to enable us to be open and extensible,” Sangani said via email. “We’re going to bring innovation to the market that will increase the number of data assets we cover and the people who will leverage and access Alation.”
With Alation, Sangani and his fellow co-founders — Aaron Kalb, Feng Niu and Venky Ganti — sought to build a service that enables data and analytics teams to capture and understand the full breadth of their data. The way Sangani sees it, most corporate leadership wants to build a “data-driven” culture but is stymied by tech hurdles and a lack of knowledge about what data they have, where it lives, whether it’s trustworthy and how to make the best use of it.
According to Forrester, somewhere between 60% and 73% of data produced by enterprises goes unused for analytics. And if a recent poll by Oracle is to be believed, 95% of people say they’re overwhelmed by the amount of data available to them in the workplace.
“With the astounding amount of data being produced today, it’s increasingly difficult for companies to collect, structure, and analyze the data they create,” Sangani said. “The modern enterprise relies on data intelligence and data integration solutions to provide access to valuable insights that feed critical business outcomes. Alation is foundational for driving digital transformation.”
Alation uses machine learning to automatically parse and organize data like technical metadata, user permissions and business descriptions from sources like Redshift, Hive, Presto, Spark and Teradata. Customers can visually track the usage of assets like business glossaries, data dictionaries and Wiki articles through the Alation platform’s reporting feature, or they can use Alation’s collaboration tools to create lists, annotations, comments and polls to organize data across different software and systems.
Alation also makes recommendations based on how information is being used and orchestrated. For example, the platform suggests ways customers can centrally manage their data and compliance policies through the use of integrations and data connectors.
“Alation’s machine learning contributes to data search, data stewardship, business glossary, and data lineage,” Sangani said. “More specifically, Alation’s behavioral analysis engine spots behavioral patterns and leverages AI and machine learning to make data more user-friendly. For example, search is simplified by highlighting the most popular assets; stewardship is eased by emphasizing the most active data sets; and governance becomes a part of workflow through flags and suggestions.”
According to IDC, the data integration and intelligence software market is valued at more than $7.9 billion and growing toward $11.6 billion over the next four years. But Alation isn’t the sole vendor. The startup’s competition includes incumbents like Informatica, IBM, SAP and Oracle, as well as newer rivals such as Collibra, Castor, Stemma, Data.World and Ataccama, all of whom offer tools for classifying and curating data at enterprise scale.
One of Alation’s advantages is sheer momentum, no doubt — its customer base includes heavyweights like Cisco, General Mills, Munich Re, Pfizer, Nasdaq and Salesforce, in addition to government agencies such as the Environmental Protection Agency and Australia’s Department of Defense. Alation counts more than 25% of the Fortune 100 as clients, touching verticals such as finance, healthcare, pharma, manufacturing, retail, insurance and tech.
In terms of revenue coming in, Sangani claims that Alation — which has more than 700 employees and expects to be at just under 800 by 2023 — is in a healthy position, pegging the firm’s cumulative-cash-burn-to-ARR ratio at around 1.5x. Despite the downturn, he asserts that customer spend is remaining strong as the demand for data catalog software grows; for the past five quarters, Alation’s ARR has increased year over year.
In another win for Alation, the investment from Databricks Ventures is strategic, Sangani says. It’ll see the two companies jointly develop engineering, data science and analytics applications that leverage both Databricks’ and Alations’ platforms.
“The most successful data intelligence platforms will be adopted by everyone. Vendors that are jack-of-all-trades, but masters of none, promise everything and succeed at little. Similarly, point products achieve limited success, but only serve to create data silos that our customers are trying to avoid. The future of data intelligence is about connectedness and integration,” Sangani said. “We know that and will continue to put our money behind our beliefs.”
Blackbird’s latest $1B AUD fund signals maturation of Australian, New Zealand venture scene • TechCrunch
The Australian and New Zealand startup community will see a boost in funding this year. Blackbird, a VC fund based in the two south Pacific countries, on Wednesday closed a fund at over AUD $1 billion, which is about USD $640 million, which the firm says is Australia’s largest fund to date.
This is Blackbird’s fifth fund, and it’s double the size of the VC’s last fund which closed in August 2020. Several institutional investors participated, including superannuation funds like AustralianSuper, Hostplus, Australia’s sovereign wealth fund, the Future Fund, New Zealand’s sovereign wealth funds and New Zealand Growth Capital Partners Elevate fund, which is a government-backed fund.
A decade ago, most Australian and in particular New Zealand institutional investors didn’t want to put their money anywhere near tech startups. Their support today signals a maturation of the Australia/New Zealand venture capital space.
“[Superannuation fund] capital can go anywhere. It can go into the best Silicon Valley VCs,” Sam Wong, a partner at Blackbird, told TechCrunch. “And so the fact that they are choosing to invest their money at this scale with an Aussie and Kiwi fund marks a moment for the ecosystem and shows that we have earned our right on the global stage to manage that capital.”
According to Wong, it makes sense for superannuation funds to back the tech space because they have horizons in the decades and can afford to be patient.
“What they really care about is high returns so people can retire in dignity,” she said. “And when you have that long-term horizon, you can seek higher return assets that don’t have liquidity profiles that, say, public markets do. And that’s exactly what we found in the Australian superannuation system — they love tech because it’s high growth, high return. It’s very long dated, and they don’t mind that it’s locked up for 10 years.”
The fund is also supported by over 270 individual investors, many of whom are tech founders and operators that Blackbird backed through earlier funds, according to the firm. Those founders will support the fund both with their own capital, but also their expertise, knowledge and connections, said Wong.
The total AUD $1 billion consists of three separate vehicles: an AUD $284 million (USD $182 million) core fund for pre-seed and seed stage Aussie companies, an AUD $668 million (USD $472 million) follow-on fund to support Blackbird portfolio companies anywhere from “Series A to the last round at Canva,” and a NZD $75 million (USD $44 million) dedicated New Zealand fund, which is also largely for pre-seed and seed stage companies.
Blackbird prides itself on cutting the earliest checks, which could be anywhere from $25,000 for a small pre-seed to up to $5 million for a seed round, said Wong. The firm’s mandate is to invest in founders with an Aussie or Kiwi connection, which usually means they’re based in those countries, but often ends up extending to those who founded companies abroad. Around 40% of Blackbird’s portfolio companies are actually headquartered in the U.S., said Phoebe Harrop, a principal at Blackbird.
The fund has already made 18 investments into startups in a broad range of industries from AI to manufacturing to e-commerce. Last month, Blackbird invested in Sonder, an employee and student wellbeing company, and Spice AI, a data and AI-driven infrastructure platform.
Blackbird said it predicts tech companies will contribute 20% of Australia’s GDP by 2032, which would be up from 8.5% today, according to the Tech Council of Australia.
“We’re here to change the culture of Australia and New Zealand’s ecosystems, to make a difference at a country level,” said Niki Scevak, partner at Blackbird, in a statement.
Twitter ad sales head resigned amid turbulent Musk takeover • TechCrunch
Twitter’s Chief Consumer Officer Sarah Personette has left the company, she wrote in a Twitter thread Tuesday morning.
Personette, who was in charge of Twitter’s ad sales business, said that she resigned on Friday, and her work access was officially cut off by Tuesday. The day before her resignation, Musk fired four key executives immediately after his takeover: CEO Parag Agrawal, CFO Ned Segal, General Counsel Sean Edgett and Head of Legal Policy, Trust and Safety Vijaya Gadde.
With Personette out of the picture, the number of remaining pre-Musk executives at Twitter is dwindling, with more key personnel rumored to be leaving as well. Jay Sullivan, Twitter’s head of product, deleted the bio on his Twitter account, which previously denoted his role at the company. The previous head of product, Kayvon Beykpour, was let go by former CEO Agrawal in May.
A former Facebook marketing VP, Personette had worked at Twitter since October 2018, when she joined as a VP of Global Client Solutions, per LinkedIn. She was promoted to Chief Customer Officer in August 2021. That role is crucial to Twitter’s business, since the majority of its revenue comes from ad sales. With Musk expected to make changes to content moderation policies, ad sales could be impacted.
As newly installed “Chief Twit” Elon Musk took over on Thursday, he posted a screenshot of a letter he wrote to Twitter advertisers, vowing that the platform “obviously cannot become a free-for-all hellscape.” Personette quote-tweeted his message, saying that he had a great conversation with the Tesla and SpaceX CEO. She added, “Our continued commitment to brand safety for advertisers remains unchanged. Looking forward to the future!”
But by the following evening, she had resigned.
“It has been the greatest privilege to serve all of you as a leader and a partner,” Personette said. “Many have heard me say this but the most important role I believe I played in the company was championing the requirements of brand safety.”
Rapyd Ventures backs Indian fintech-as-a-service startup Decentro • TechCrunch
India’s Decentro, the Y Combinator-backed startup that helps companies enter the fintech market by deploying its APIs, has raised $4.7 million in a Series A round.
The Bengaluru-based startup offers banking and payments APIs that allow development of fintech products such as banking, payment cards, neobanking and collections and payout services in a short period of time. Decentro has partnered with scores of industry players including Axis Bank, ICICI Bank, Kotak Mahindra Bank, Yes Bank, Visa, RuPay, Quickwork, Equifax, Aadhaar and National Securities Depository Limited (NSDL) to offer solutions for prepaid payment instruments, no-code workflows, conversational banking via WhatsApp and enable document verification and KYC process.
“Whenever a fintech startup or a company wants to launch a new product in the market, it takes them a minimum of a few months to launch. And it purely has to do with the bank processes, the way the bank runs the process, as well as the tech of the bank. It’s not so great. That’s essentially the problem we are solving,” said Rohit Taneja, co-founder and CEO, Decentro, in an interview with TechCrunch.
Taneja, who has previously co-founded social payments platform Mypoolin, which was acquired by Cupertino-based financial services company Wibmo, and spent eight years in the fintech market, co-founded Decentro with Pratik Daukhane in 2020 — after personally facing all the problems he wants to address. He considers Cashfree and PineLabs-owned Setu among the key competitors for the startup but believes that it’s differentiating with “solution-driven enterprise customer base” and “superior” product experience.
The startup has already amassed over 250 customers in commerce and fintech sectors. Some of these include Freo, Mobile Premier League, FamPay, CreditWise, Uni Cards and BharatX.
Decentro, which has a headcount of over 40 people, offers products to let companies create virtual, business and escrow accounts, enable payments and provide lending. The available products comply with all the latest regulations in the country, the startup said.
The Series A round of Decentro is led by Rapyd Ventures, the venture arm of the UK fintech-as-a-service giant, along with participation from Leonis VC and Uncorrelated Ventures. Indian angel investors including CRED founder Kunal Shah, Groww co-founder and CEO Lalit Keshre, Gupshup co-founder and CEO Beerud Sheth and former CBO of BharatPe Pratekk Agarwaal also participated in the funding round.
Taneja told TechCrunch that the startup aims to utilize the fresh funding to go deeper into its partnership with banks and enter categories including large enterprises. It also plans to acquire licenses and launch in Singapore to expand beyond India eventually.
“Building their innovation layer in India first gives Decentro a great base to build scalable innovations that can be expanded as other emerging markets modernize their own infrastructure. We’re excited to support Decentro as they scale and expand,” said Joel Yarbrough, MD of Rapyd Ventures and Rapyd’s VP of Asia Pacific, in a prepared statement.
Before the latest funding round, Decentro had raised a total of $1.7 million in seed and angel rounds. The seed round, which closed in October 2020, included investments from Y Combinator and FundersClub.
Since then, the startup claims its valuation has increased by 3.3X and revenues have grown by more than 35X. Taneja, however, did not reveal any specifics about the valuation.
Dcentro’s API transactional volumes have also been growing by 50 to 70% every quarter since early 2021, with an average of 70 million annualized API transactions recorded over the last 12 months, it said. The startup is also profitable, the co-founder said.
Contraline erects $7.2M for contraceptive implants for men
The cervix industry has had implants to prevent pregnancy since the late 1960s, but there hasn’t exactly been stiff competition to slow down the fallopian swim team at its source. In fact, Contraline claims it is the first major innovation in this space since the vasectomy was performed on a human some 125 years ago. The company calls its product ADAM, and it just raised a wad of cash to continue its trials.
“The first-in-human male contraceptive implant is a major clinical milestone that opens up new possibilities for men who wish to take contraception into their own hands,” said Kevin Eisenfrats, Co-founder and CEO of Contraline. “The patient demand for the ADAM Study has been tremendous, with the entire trial oversubscribing within three weeks of opening enrollment. We are looking forward to advancing ADAM through clinical development and bringing this product to market to transform how people think about contraception.”
The company just raised $7.2 million in funding led by GV. The goal is to advance its in-human clinical trials of its injectable hydrogel designed to provide long-lasting, non-permanent contraception for men. The product uses a “hydrogel” designed to occlude sperm flow through the vas deferens for a predefined period of time, eventually degrading and thus offering a non-permanent contraceptive option.
The company suggests that the contraceptive is long-lasting but non-permanent, and claims it has no hormonal impact on the patients. The company told TechCrunch that four men were implanted with ADAM at a hospital in Australia, using a minimally invasive, no-scalpel approach, with ADAM being injected using a patent-pending delivery device.
The procedure marks the first patient implanted in “The ADAM Study,” which is being conducted under Human Research Ethics Committee approval. The ADAM Study is assessing the safety of the ADAM Hydrogel, while monitoring the semen parameters of the study subjects over three years.
“Contraline has the potential to fundamentally change the market for contraception,” said Cathy Friedman, executive venture partner at GV. “We look forward to working with the team as they continue developing a long-acting, reversible male contraceptive that empowers more people with more choices over family planning.”
Contraline’s study in Australia continues, and its next, longer-term goal is to run a second study with a larger group of patients in the United States.
Contraline erects $7.2M for contraceptive implants for men by Haje Jan Kamps originally published on TechCrunch
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