machine learning
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Splice gets $55 million for its software bringing beats from bedrooms to bandstands
Splice, the New York-based, AI-infused, beat-making software service for music producers created by the founder of GroupMe, has managed to sample another $55 million in financing from investors for its wildly popular service.
The GitHub for music producers ranging from Hook N Sling, Mr Hudson SLY, and Steve Solomon to TechCrunch’s own Megan Rose Dickey, Splice gained a following for its ability to help electronic dance music creators save, share, collaborate and remix music.
The company’s popularity has made it from bedroom DJs to the Goldman Sachs boardroom as the financial services giant joined MUSIC, a joint venture between the music executive Matt Pincus and boutique financial services firm Liontree in leading the company’s latest $55 million round. The company’s previous investors include USV, True Ventures, DFJ Growth and Flybridge.
“The music creation process is going through a digital transformation. Artists are flocking to solutions that offer a user-friendly, collaborative, and affordable platform for music creation,” said Stephen Kerns, a VP with Goldman Sachs’ GS Growth, in a statement. “With 4 million users, Splice is at the forefront of this transformation and is beloved by the creator community. We’re thrilled to be partnering with Steve Martocci and his team at Splice.”
Splice’s financing follows an incredibly acquisitive 2020 for the company, which saw it acquiring music technology companies Audiaire and Superpowered.
In addition to the financing, Splice also nabbed Kakul Srivastava, the vice president of Adobe Creative Cloud Experience and Engagement as a director for its board.
The funding news comes on the heels of Splice’s recent acquisitions of music-tech companies Audiaire and Superpowered, creating more ways to improve and inspire the audio and music-making process. Splice is also pleased to announce that Kakul Srivastava has joined the company’s board.
Steve Martocci at TechCrunch Disrupt in 2016. Image Credits: Getty Images
Splice’s beefed up balance sheet comes as new entrants have started vying for a slice of Splice’s music-making market. These are companies like hardware maker Native Instruments, which launched the Sounds.com marketplace last year, and there’s also Arcade by Output that’s pitching a similar service.
Meanwhile, Splice continues to invest in new technology to make producers’ lives easier. In November 2019 it unveiled its artificial intelligence product that lets producers match samples from different genres using machine learning techniques to find the matches.
“My job is to keep as many people inspired to create as possible,” Splice founder and chief executive Steve Martocci told TechCrunch.
It’s another win for the serial entrepreneur who famously sold his TechCrunch Disrupt Hackathon chat app GroupMe to Skype for $85 million just a year after launching.
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Krisp nearly triples fundraise with $9M expansion after blockbuster 2020
Krisp, a startup that uses machine learning to remove background noise from audio in real time, has raised $9M as an extension of its $5M A round announced last summer. The extra money followed big traction in 2020 for the Armenian company, which grew its customers and revenue by more than an order of magnitude.
TechCrunch first covered Krisp when it was just emerging from UC Berkeley’s Skydeck accelerator, and co-founder Davit Baghdasaryan was relatively freshly out of his previous role at Twilio. The company’s pitch when I chatted with them in the shared office back then was simple and remains the core of what they offer: isolation of the human voice from any background noise (including other voices) so that audio contains only the former.
It probably comes as no surprise, then, that the company appears to have benefited immensely from the shift to virtual meetings and other trends accelerated by the pandemic. To be specific, Baghdasaryan told me that 2020 brought the company a 20x increase in active users, a 23x increase in enterprise accounts and 13x improvement of annual recurring revenue.
The rise in virtual meetings — often in noisy places like, you know, homes — has led to significant uptake across multiple industries. Krisp now has more than 1,200 enterprise customers, Baghdasaryan said: banks, HR platforms, law firms, call centers — anyone who benefits from having a clear voice on the line (“I guess any company qualifies,” he added). Enterprise-oriented controls like provisioning and central administration have been added to make it easier to integrate.
Image Credits: Krisp
B2B revenue recently eclipsed B2C; the latter was likely popularized by Krisp’s inclusion as an option in popular gaming (and increasingly beyond) chat app Discord, though of course users of a free app being given a bonus product for free aren’t always big converters to “pro” tiers of a product.
But the company hasn’t been standing still, either. While it began with a simple feature set (turning background noise on and off, basically) Krisp has made many upgrades to both its product and infrastructure.
Noise cancellation for high-fidelity voice channels makes the software useful for podcasters and streamers, and acoustic correction (removing room echos) simplifies those setups quite a bit as well. Considering the amount of people doing this and the fact that they’re often willing to pay, this could be a significant source of income.
The company plans to add cross-service call recording and analysis; since it sits between the system’s sound drivers and the application, Krisp can easily save the audio and other useful metadata (How often did person A talk versus person B? What office locations are noisiest?). And the addition of voice cancellation — other people’s voices, that is — could be a huge benefit for people who work, or anticipate returning to work, in crowded offices and call centers.
Part of Krisp’s allure is the ability to run locally and securely on many platforms with very low overhead. But companies with machine learning-based products can stagnate quickly if they don’t improve their infrastructure or build more efficient training flows — Lengoo, for instance, is taking on giants in the translation industry with better training as more or less its main advantage.
Krisp has been optimizing and reoptimizing its algorithms to run efficiently on both Intel and ARM architectures, and decided to roll out its own servers for training its models instead of renting from the usual suspects.
“AWS, Azure and Google Cloud turned out to be too expensive,” Baghdasaryan said. “We have invested in building a data center with Nvidia’s latest A100s in them. This will make our experimentation faster, which is crucial for ML companies.”
Baghdasaryan was also emphatic in his satisfaction with the team in Armenia, where he and his co-founder Arto Minasyan are from, and where the company has focused its hiring, including the 25-strong research team. “By the end of 2021 it will be a 45-member team, all in Armenia,” he said. “We are super happy with the math, physics and engineering talent pool there.”
The funding amounts to $14 million if you combine the two disparate parts of the A round, the latter of which was agreed to just three months after the first. That’s a lot of money, of course, but may seem relatively modest for a company with a thousand enterprise customers and revenue growing by more than 2,000% year over year.
Baghdasaryan said they just weren’t ready to take on a whole B round, with all that involves. They do plan a new fundraise later this year when they’ve reached $15 million ARR, a goal that seems perfectly reasonable given their current charts.
Of course startups with this kind of growth tend to get snapped up by larger concerns, but despite a few offers Baghdasaryan says he’s in it for the long haul — and a multibillion dollar market.
The rush to embrace the new virtual work economy may have spurred Krisp’s growth spurt, but it’s clear that neither the company nor the environment that let it thrive are going anywhere.
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Notable Health seeks to improve COVID-19 vaccine administration through intelligent automation
Efficient and cost-effective vaccine distribution remains one of the biggest challenges of 2021, so it’s no surprise that startup Notable Health wants to use their automation platform to help. Initially started to address the nearly $250 billion annual administrative costs in healthcare, Notable Health launched in 2017 to use automation to replace time-consuming and repetitive simple tasks in health industry admin. In early January of this year, they announced plans to use that technology as a way to help manage vaccine distribution.
“As a physician, I saw firsthand that with any patient encounter, there are 90 steps or touch points that need to occur,” said Notable Health Medical Director Muthu Alagappan in an interview. “It’s our hypothesis that the vast majority of those points can be automated.”
Notable Health’s core technology is a platform that uses robotic process automation (RPA), natural language processing (NLP) and machine learning to find eligible patients for the COVID-19 vaccine. Combined with data provided by hospital systems’ electronic health records, the platform helps those qualified to receive the vaccine set up appointments and guides them to other relevant educational resources.
“By leveraging intelligent automation to identify, outreach, educate and triage patients, health systems can develop efficient and equitable vaccine distribution workflows,” said Notable Health strategic advisor and Biden Transition COVID-19 Advisory Board Member Dr. Ezekiel Emanuel, in a press release.
Making vaccine appointments has been especially difficult for older Americans, many of whom have reportedly struggled with navigating scheduling websites. Alagappan sees that as a design problem. “Technology often gets a bad reputation, because it’s hampered by the many bad technology experiences that are out there,” he said.
Instead, he thinks Notable Health has kept the user in mind through a more simplified approach, asking users only for basic and easy-to-remember information through a text message link. “It’s that emphasis on user-centric design that I think has allowed us to still have really good engagement rates even with older populations,” he said.
While the startup’s platform will likely help hospitals and health systems develop a more efficient approach to vaccinations, its use of RPA and NLP holds promise for future optimization in healthcare. Leaders of similar technology in other industries have already gone on to have multibillion dollar valuations and continue to attract investors’ interest.
Artificial intelligence is expected to grow in healthcare over the next several years, but Alagappan argues that combining that with other, more readily available intelligent technologies is also an important step toward improved care. “When we say intelligent automation, we’re really referring to the marriage of two concepts: artificial intelligence — which is knowing what to do — and robotic process automation — which is knowing how to do it,” he said. That dual approach is what he says allows Notable Health to bypass administrative bottlenecks in healthcare, instructing bots to carry out those tasks in an efficient and adaptable way.
So far, Notable Health has worked with several hospital systems across multiple states in using their platform for vaccine distribution and scheduling, and are now using the platform to reach out to tens of thousands of patients per day.
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Base Operations raises $2.2 million to modernize physical enterprise security
Typically when we talk about tech and security, the mind naturally jumps to cybersecurity. But equally important, especially for global companies with large, multinational organizations, is physical security — a key function at most medium-to-large enterprises, and yet one that to date, hasn’t really done much to take advantage of recent advances in technology. Enter Base Operations, a startup founded by risk management professional Cory Siskind in 2018. Base Operations just closed their $2.2 million seed funding round and will use the money to capitalize on its recent launch of a street-level threat mapping platform for use in supporting enterprise security operations.
The funding, led by Good Growth Capital and including investors like Magma Partners, First In Capital, Gaingels and First Round Capital founder Howard Morgan, will be used primarily for hiring, as Base Operations looks to continue its team growth after doubling its employe base this past month. It’ll also be put to use extending and improving the company’s product and growing the startup’s global footprint. I talked to Siskind about her company’s plans on the heels of this round, as well as the wider opportunity and how her company is serving the market in a novel way.
“What we do at Base Operations is help companies keep their people in operation secure with ‘Micro Intelligence,’ which is street-level threat assessments that facilitate a variety of routine security tasks in the travel security, real estate and supply chain security buckets,” Siskind explained. “Anything that the chief security officer would be in charge of, but not cyber — so anything that intersects with the physical world.”
Siskind has firsthand experience about the complexity and challenges that enter into enterprise security since she began her career working for global strategic risk consultancy firm Control Risks in Mexico City. Because of her time in the industry, she’s keenly aware of just how far physical and political security operations lag behind their cybersecurity counterparts. It’s an often overlooked aspect of corporate risk management, particularly since in the past it’s been something that most employees at North American companies only ever encounter periodically when their roles involve frequent travel. The events of the past couple of years have changed that, however.
“This was the last bastion of a company that hadn’t been optimized by a SaaS platform, basically, so there was some resistance and some allegiance to legacy players,” Siskind told me. “However, the events of 2020 sort of turned everything on its head, and companies realized that the security department, and what happens in the physical world, is not just about compliance — it’s actually a strategic advantage to invest in those sort of services, because it helps you maintain business continuity.”
The COVID-19 pandemic, increased frequency and severity of natural disasters, and global political unrest all had significant impact on businesses worldwide in 2020, and Siskind says that this has proven a watershed moment in how enterprises consider physical security in their overall risk profile and strategic planning cycles.
“[Companies] have just realized that if you don’t invest [in] how to keep your operations running smoothly in the face of rising catastrophic events, you’re never going to achieve the profits that you need, because it’s too choppy, and you have all sorts of problems,” she said.
Base Operations addresses this problem by taking available data from a range of sources and pulling it together to inform threat profiles. Their technology is all about making sense of the myriad stream of information we encounter daily — taking the wash of news that we sometimes associate with “doom-scrolling” on social media, for instance, and combining it with other sources using machine learning to extrapolate actionable insights.
Those sources of information include “government statistics, social media, local news, data from partnerships, like NGOs and universities,” Siskind said. That data set powers their Micro Intelligence platform, and while the startup’s focus today is on helping enterprises keep people safe, while maintaining their operations, you can easily see how the same information could power everything from planning future geographical expansion, to tailoring product development to address specific markets.
Siskind saw there was a need for this kind of approach to an aspect of business that’s essential, but that has been relatively slow to adopt new technologies. From her vantage point two years ago, however, she couldn’t have anticipated just how urgent the need for better, more scalable enterprise security solutions would arise, and Base Operations now seems perfectly positioned to help with that need.
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Immunai raises $60M as it expands from improving immune therapies to discovering new ones, too
Just three years after its founding, biotech startup Immunai has raised $60 million in Series A funding, bringing its total raised to over $80 million. Despite its youth, Immunai has already established the largest database in the world for single cell immunity characteristics, and it has already used its machine learning-powered immunity analysts platform to enhance the performance of existing immunotherapies. Aided by this new funding, it’s now ready to expand into the development of entirely new therapies based on the strength and breadth of its data and ML.
Immunai’s approach to developing new insights around the human immune system uses a “multiomic” approach — essentially layering analysis of different types of biological data, including a cell’s genome, microbiome, epigenome (a genome’s chemical instruction set) and more. The startup’s unique edge is in combining the largest and richest data set of its type available, formed in partnership with world-leading immunological research organizations, with its own machine learning technology to deliver analytics at unprecedented scale.
“I hope it doesn’t sound corny, but we don’t have the luxury to move more slowly,” explained Immunai co-founder and CEO Noam Solomon in an interview. “Because I think that we are in kind of a perfect storm, where a lot of advances in machine learning and compute computations have led us to the point where we can actually leverage those methods to mine important insights. You have a limit or ceiling to how fast you can go by the number of people that you have — so I think with the vision that we have, and thanks to our very large network between MIT and Cambridge to Stanford in the Bay Area, and Tel Aviv, we just moved very quickly to harness people to say, let’s solve this problem together.”
Solomon and his co-founder and CTO Luis Voloch both have extensive computer science and machine learning backgrounds, and they initially connected and identified a need for the application of this kind of technology in immunology. Scientific co-founder and SVP of Strategic Research Danny Wells then helped them refine their approach to focus on improving efficacy of immunotherapies designed to treat cancerous tumors.
Immunai has already demonstrated that its platform can help identify optimal targets for existing therapies, including in a partnership with the Baylor College of Medicine where it assisted with a cell therapy product for use in treating neuroblastoma (a type of cancer that develops from immune cells, often in the adrenal glands). The company is now also moving into new territory with therapies, using its machine learning platform and industry-leading cell database to new therapy discovery — not only identifying and validating targets for existing therapies, but helping to create entirely new ones.
“We’re moving from just observing cells, but actually to going and perturbing them, and seeing what the outcome is,” explained Voloch. This, from the computational side, later allows us to move from correlative assessments to actually causal assessments, which makes our models a lot more powerful. Both on the computational side and on the lab side, this are really bleeding edge technologies that I think we will be the first to really put together at any kind of real scale.”
“The next step is to say, ‘Okay, now that we understand the human immune profile, can we develop new drugs?’,” said Solomon. “You can think about it like we’ve been building a Google Maps for the immune system for a few years — so we are mapping different roads and paths in the immune system. But at some point, we figured out that there are certain roads or bridges that haven’t been built yet. And we will be able to support building new roads and new bridges, and hopefully leading from current states of disease or cities of disease, to building cities of health.”
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Weights & Biases raises $45M for its machine learning tools
Weights & Biases, a startup building tools for machine learning practitioners, is announcing that it has raised $45 million in Series B funding.
The company was founded by Lukas Biewald, Chris Van Pelt and Shawn Lewis — Biewald and Van Pelt previously founded CrowdFlower/Figure Eight (acquired by Appen). Weights & Biases says it now has more than 70,000 users at more than 200 enterprises.
Biewald (whom I’ve known since college) argued that while machine learning practitioners are often compared to software developers, “they’re more like scientists in some ways than engineers.” It’s a process that involves numerous experiments, and Weights & Biases’ core product allows practitioners to track those experiments, while the company also offers tools around data set versioning, model evaluation and pipeline management.
“If you have a model that’s controlling a self-driving car and the car crashes, you really want to know what happened,” Biewald said. “If you built that model years ago and you’ve run all these experiments since then, it can be hard to systematically trace through what happened” unless you’re using experiment tracking.
He described the startup as “an early leader” in this market, and as competing tools emerge, he said it’s also differentiated because it is “completely focused on the ML practitioner” rather than top-down enterprise sales. Similarly, he said that as machine learning has been adopted more widely, Weights & Biases is occasionally confronted by a “high-class problem.”
Image Credits: Weights & Biases
“We’re not interested in selling to companies that are doing machine learning for machine learning’s sake,” Biewald said. “With some companies, there’s a mandate from the CEO to sprinkle some machine learning in the company. That’s just really depressing to me, to not have any impact. But I would actually say the vast majority of companies that we talk to really do something useful.”
For example, he said agriculture giant John Deere is using the startup’s platform to continually improve the way it uses robotics to spray fertilizer, rather than pesticides, to kill weeds and pests. And there are pharmaceutical companies using the platform for how they model how different molecules will behave.
Weights & Biases previously raised $20 million in funding. The new round was led by Insight Partners, with participation from Coatue, Trinity Ventures and Bloomberg Beta. Insight’s George Mathew is joining the board of directors.
“I’ve never seen a MLOps category leader with such a high NPS and deep customer focus as Weights and Biases,” Mathew said in a statement. “It’s an honor to make my first investment at Insight to serve an ML practitioner user-base that grew 60x these last two years.”
The startup says it will use the funding to continue hiring in engineering, growth, sales and customer success.
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Pinecone lands $10M seed for purpose-built machine learning database
Pinecone, a new startup from the folks who helped launch Amazon SageMaker, has built a vector database that generates data in a specialized format to help build machine learning applications faster, something that was previously only accessible to the largest organizations. Today the company came out of stealth with a new product and announced a $10 million seed investment led by Wing Venture Capital.
Company co-founder Edo Liberty says that he started the company because of this fundamental belief that the industry was being held back by the lack of wider access to this type of database. “The data that a machine learning model expects isn’t a JSON record, it’s a high dimensional vector that is either a list of features or what’s called an embedding that’s a numerical representation of the items or the objects in the world. This [format] is much more semantically rich and actionable for machine learning,” he explained.
He says that this is a concept that is widely understood by data scientists, and supported by research, but up until now only the biggest and technically superior companies like Google or Pinterest could take advantage of this difference. Liberty and his team created Pinecone to put that kind of technology in reach of any company.
The startup spent the last couple of years building the solution, which consists of three main components. The main piece is a vector engine to convert the data into this machine-learning ingestible format. Liberty says that this is the piece of technology that contains all the data structures and algorithms that allow them to index very large amounts of high dimensional vector data, and search through it in an efficient and accurate way.
The second is a cloud hosted system to apply all of that converted data to the machine learning model, while handling things like index lookups along with the pre- and post-processing — everything a data science team needs to run a machine learning project at scale with very large workloads and throughputs. Finally, there is a management layer to track all of this and manage data transfer between source locations.
One classic example Liberty uses is an eCommerce recommendation engine. While this has been a standard part of online selling for years, he believes using a vectorized data approach will result in much more accurate recommendations and he says the data science research data bears him out.
“It used to be that deploying [something like a recommendation engine] was actually incredibly complex, and […] if you have access to a production grade database, 90% of the difficulty and heavy lifting in creating those solutions goes away, and that’s why we’re building this. We believe it’s the new standard,” he said.
The company currently has 10 people including the founders, but the plan is to double or even triple that number, depending on how the year goes. As he builds his company as an immigrant founder — Liberty is from Israel — he says that diversity is top of mind. He adds that it’s something he worked hard on at his previous positions at Yahoo and Amazon as he was building his teams at those two organizations. One way he is doing that is in the recruitment process. “We have instructed our recruiters to be proactive [in finding more diverse applicants], making sure they don’t miss out on great candidates, and that they bring us a diverse set of candidates,” he said.
Looking ahead to post-pandemic, Liberty says he is a bit more traditional in terms of office versus home, and that he hopes to have more in-person interactions. “Maybe I’m old fashioned but I like offices and I like people and I like to see who I work with and hang out with them and laugh and enjoy each other’s company, and so I’m not jumping on the bandwagon of ‘let’s all be remote and work from home’.”
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Extra Crunch roundup: 2 VC surveys, Tesla’s melt up, The Roblox Gambit, more
This has been quite a week.
Instead of walking backward through the last few days of chaos and uncertainty, here are three good things that happened:
- Google employee Sara Robinson combined her interest in machine learning and baking to create AI-generated hybrid treats.
- A breakthrough could make water desalination 30%-40% more effective.
- Bianca Smith will become the first Black woman to coach a professional baseball team.
Despite many distractions in our first full week of the new year, we published a full slate of stories exploring different aspects of entrepreneurship, fundraising and investing.
We’ve already gotten feedback on this overview of subscription pricing models, and a look back at 2020 funding rounds and exits among Israel’s security startups was aimed at our new members who live and work there, along with international investors who are seeking new opportunities.
Plus, don’t miss our first investor surveys of 2021: one by Lucas Matney on social gaming, and another by Mike Butcher that gathered responses from Portugal-based investors on a wide variety of topics.
Thanks very much for reading Extra Crunch this week. I hope we can all look forward to a nice, boring weekend with no breaking news alerts.
Walter Thompson
Senior Editor, TechCrunch
@yourprotagonist
Full Extra Crunch articles are only available to members
Use discount code ECFriday to save 20% off a one- or two-year subscription
The Roblox Gambit
Image Credits: Nigel Sussman (opens in a new window)
In February 2020, gaming platform Roblox was valued at $4 billion, but after announcing a $520 million Series H this week, it’s now worth $29.5 billion.
“Sure, you could argue that Roblox enjoyed an epic 2020, thanks in part to COVID-19,” writes Alex Wilhelm this morning. “That helped its valuation. But there’s a lot of space between $4 billion and $29.5 billion.”
Alex suggests that Roblox’s decision to delay its IPO and raise an enormous Series H was a grandmaster move that could influence how other unicorns will take themselves to market. “A big thanks to the gaming company for running this experiment for us.”
I asked him what inspired the headline; like most good ideas, it came to him while he was trying to get to sleep.
“I think that I had ‘The Queen’s Gambit’ somewhere in my head, so that formed the root of a little joke with myself. Roblox is making a strategic wager on method of going public. So, ‘gambit’ seems to fit!”
8 investors discuss social gaming’s biggest opportunities
Image Credits: Erik Von Weber (opens in a new window) / Getty Images
For our first investor survey of the year, Lucas Matney interviewed eight VCs who invest in massively multiplayer online games to discuss 2021 trends and opportunities:
- Hope Cochran, Madrona Venture Group
- Daniel Li, Madrona Venture Group
- Niko Bonatsos, General Catalyst
- Ethan Kurzweil, Bessemer Venture Partners
- Sakib Dadi, Bessemer Venture Partners
- Jacob Mullins, Shasta Ventures
- Alice Lloyd George, Rogue
- Gigi Levy-Weiss, NFX
Having moved far beyond shooters and sims, platforms like Twitch, Discord and Fortnite are “where culture is created,” said Daniel Li of Madrona.
Rep. Alexandria Ocasio-Cortez uses Twitch to explain policy positions, major musicians regularly perform in-game concerts on Fortnite and in-game purchases generated tens of billions last year.
“Gaming is a unique combination of science and art, left and right brain,” said Gigi Levy-Weiss of NFX. “It’s never just science (i.e., software and data), which is why many investors find it hard.”
How to convert customers with subscription pricing
Image Credits: C.J. Burton (opens in a new window) / Getty Images
Startups that lack insight into their sales funnel have high churn, low conversion rates and an inability to adapt or leverage changes in customer behavior.
If you’re hoping to convert and retain customers, “reinforcing your value proposition should play a big part in every level of your customer funnel,” says Joe Procopio, founder of Teaching Startup.
What is up with Tesla’s value?
Image Credits: Bloomberg (opens in a new window) / Getty Images
Alex Wilhelm followed up his regular Friday column with another story that tries to find a well-grounded rationale for Tesla’s sky-high valuation of approximately $822 billion.
Meanwhile, GM just unveiled a new logo and tagline.
As ever, I learned something new while editing: A “melt up” occurs when investors start clamoring for a particular company because of acute FOMO (the fear of missing out).
Delivering 500,000 cars in 2020 was “impressive,” says Alex, who also acknowledged the company’s ability to turn GAAP profits, but “pride cometh before the fall, as does a melt up, I think.”
Note: This story has Alex’s original headline, but I told him I would replace the featured image with a photo of someone who had very “richest man in the world” face.
How Segment redesigned its core systems to solve an existential scaling crisis
Image Credits: piranka / Getty Images
On Tuesday, enterprise reporter Ron Miller covered a major engineering project at customer data platform Segment called “Centrifuge.”
“Its purpose was to move data through Segment’s data pipes to wherever customers needed it quickly and efficiently at the lowest operating cost,” but as Ron reports, it was also meant to solve “an existential crisis for the young business,” which needed a more resilient platform.
Dear Sophie: Banging my head against the wall understanding the US immigration system
Image Credits: Sophie Alcorn
Dear Sophie:
Now that the U.S. has a new president coming in whose policies are more welcoming to immigrants, I am considering coming to the U.S. to expand my company after COVID-19. However, I’m struggling with the morass of information online that has bits and pieces of visa types and processes.
Can you please share an overview of the U.S. immigration system and how it works so I can get the big picture and understand what I’m navigating?
— Resilient in Romania
The first “Dear Sophie” column of each month is available on TechCrunch without a paywall.
Revenue-based financing: The next step for private equity and early-stage investment
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For founders who aren’t interested in angel investment or seeking validation from a VC, revenue-based investing is growing in popularity.
To gain a deeper understanding of the U.S. RBI landscape, we published an industry report on Wednesday that studied data from 134 companies, 57 funds and 32 investment firms before breaking out “specific verticals and business models … and the typical profile of companies that access this form of capital.”
Lisbon’s startup scene rises as Portugal gears up to be a European tech tiger
Image Credits: Westend61 (opens in a new window)/ Getty Images
Mike Butcher continues his series of European investor surveys with his latest dispatch from Lisbon, where a nascent startup ecosystem may get a Brexit boost.
Here are the Portugal-based VCs he interviewed:
- Cristina Fonseca, partner, Indico Capital Partners
- Pedro Ribeiro Santos, partner, Armilar Venture Partners
- Tocha, partner, Olisipo Way
- Adão Oliveira, investment manager, Portugal Ventures
- Alexandre Barbosa, partner, Faber
- António Miguel, partner, Mustard Seed MAZE
- Jaime Parodi Bardón, partner, impACT NOW Capital
- Stephan Morais, partner, Indico Capital Partners
- Gavin Goldblatt, managing partner, Portugal Gateway
How late-stage edtech companies are thinking about tutoring marketplaces
Image Credits: John Lund (opens in a new window)/ Getty Images
How do you scale online tutoring, particularly when demand exceeds the supply of human instructors?
This month, Chegg is replacing its seven-year-old marketplace that paired students with tutors with a live chatbot.
A spokesperson said the move will “dramatically differentiate our offerings from our competitors and better service students,” but Natasha Mascarenhas identified two challenges to edtech automation.
“A chatbot won’t work for a student with special needs or someone who needs to be handheld a bit more,” she says. “Second, speed tutoring can only work for a specific set of subjects.”
Decrypted: How bad was the US Capitol breach for cybersecurity?
Image Credits: Treedeo (opens in a new window) / Getty Images
While I watched insurrectionists invade and vandalize the U.S. Capitol on live TV, I noticed that staffers evacuated so quickly, some hadn’t had time to shut down their computers.
Looters even made off with a laptop from Senator Jeff Merkley’s office, but according to security reporter Zack Whittaker, the damages to infosec wasn’t as bad as it looked.
Even so, “the breach will likely present a major task for Congress’ IT departments, which will have to figure out what’s been stolen and what security risks could still pose a threat to the Capitol’s network.”
Extra Crunch’s top 10 stories of 2020
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On New Year’s Eve, I made a list of the 10 “best” Extra Crunch stories from the previous 12 months.
My methodology was personal: From hundreds of posts, these were the 10 I found most useful, which is my key metric for business journalism.
Some readers are skeptical about paywalls, but without being boastful, Extra Crunch is a premium product, just like Netflix or Disney+. I know, we’re not as entertaining as a historical drama about the reign of Queen Elizabeth II or a space western about a bounty hunter. But, speaking as someone who’s worked at several startups, Extra Crunch stories contain actionable information you can use to build a company and/or look smart in meetings — and that’s worth something.
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SilviaTerra wants to bring the benefits of carbon offsets to every landowner everywhere
Zack Parisa and Max Nova, the co-founders of the carbon offset company SilviaTerra, have spent the last decade working on a way to democratize access to revenue-generating carbon offsets.
As forestry credits become a big, booming business on the back of multibillion-dollar commitments from some of the world’s biggest companies to decarbonize their businesses, the kinds of technologies that the two founders have dedicated 10 years of their lives to building are only going to become more valuable.
That’s why their company, already a profitable business, has raised $4.4 million in outside funding led by Union Square Ventures and Version One Ventures, along with Salesforce founder and the driving force between the One Trillion Trees Initiative, Marc Benioff .
“Key to addressing the climate crisis is changing the balance in the so-called carbon cycle. At present, every year we are adding roughly 5 gigatons of carbon to the atmosphere. Since atmospheric carbon acts as a greenhouse gas this increases the energy that’s retained rather than radiated back into space which causes the earth to heat up,” writes Union Square Ventures managing partner Albert Wenger in a blog post. “There will be many ways such drawdown occurs and we will write about different approaches in the coming weeks (such as direct air capture and growing kelp in the oceans). One way that we understand well today and can act upon immediately are forests. The world’s forests today absorb a bit more than one gigatons of CO2 per year out of the atmosphere and turn it into biomass. We need to stop cutting and burning down existing forests (including preventing large scale forest fires) and we have to start planting more new trees. If we do that, the total potential for forests is around 4 to 5 gigatons per year (with some estimates as high as 9 gigatons).”
For the two founders, the new funding is the latest step in a long journey that began in the woods of Northern Alabama, where Parisa grew up.
After attending Mississippi State for forestry, Parisa went to graduate school at Yale, where he met Louisville, Kentucky native Max Nova, a computer science student who joined with Parisa to set up the company that would become SilviaTerra.
SilviaTerra co-founders Max Nova and Zack Parisa. Image Credit: SilviaTerra
The two men developed a way to combine satellite imagery with field measurements to determine the size and species of trees in every acre of forest.
While the first step was to create a map of every forest in the U.S., the ultimate goal for both men was to find a way to put a carbon market on equal footing with the timber industry. Instead of cutting trees for cash, potentially landowners could find out how much it would be worth to maintain their forestland. As the company notes, forest management had previously been driven by the economics of timber harvesting, with over $10 billion spent in the U.S. each year.
The founders at SilviaTerra thought that the carbon market could be equally as large, but it’s hard for most landowners to access. Carbon offset projects can cost as much as $200,000 to put together, which is more than the value of the smaller offset projects for landowners like Parisa’s own family and the 40 acres they own in the Alabama forests.
There had to be a better way for smaller landowners to benefit from carbon markets too, Parisa and Nova thought.
To create this carbon economy, there needed to be a single source of record for every tree in the U.S. and while SilviaTerra had the technology to make that map, they lacked the compute power, machine learning capabilities and resources to build the map.
That’s where Microsoft’s AI for Earth program came in.
Working with AI for Earth, SilviaTierra created their first product, Basemap, to process terabytes of satellite imagery to determine the sizes and species of trees on every acre of America’s forestland. The company also worked with the U.S. Forestry Service to access their data, which was used in creating this holistic view of the forest assets in the U.S.
With the data from Basemap in hand, the company has created what it calls the Natural Capital Exchange. This program uses SilviaTerra’s unparalleled access to information about local forests, and the knowledge of how those forests are currently used to supply projects that actually represent land that would have been forested were it not for the offset money coming in.
Currently, many forestry projects are being passed off to offset buyers as legitimate offsets on land that would never have been forested in the first place — rendering the project meaningless and useless in any real way as an offset for carbon dioxide emissions.
“It’s a bloodbath out there,” said Nova of the scale of the problem with fraudulent offsets in the industry. “We’re not repackaging existing forest carbon projects and trying to connect the demand side with projects that already exist. Use technology to unlock a new supply of forest carbon offset.”
The first Natural Capital Exchange project was actually launched and funded by Microsoft back in 2019. In it, 20 Western Pennsylvania land owners originated forest carbon credits through the program, showing that the offsets could work for landowners with 40 acres, or, as the company said, 40,000.
Landowners involved in SilviaTerra’s pilot carbon offset program paid for by Microsoft. Image Credit: SilviaTerra
“We’re just trying to get inside every landowners annual economic planning cycle,” said Nova. “There’s a whole field of timber economics… and we’re helping answer the question of given the price of timber, given the price of carbon does it make sense to reduce your planned timber harvests?”
Ultimately, the two founders believe that they’ve found a way to pay for the total land value through the creation of data around the potential carbon offset value of these forests.
It’s more than just carbon markets, as well. The tools that SilviaTerra have created can be used for wildfire mitigation as well. “We’re at the right place at the right time with the right data and the right tools,” said Nova. “It’s about connecting that data to the decision and the economics of all this.”
The launch of the SilviaTerra exchange gives large buyers a vetted source to offset carbon. In some ways it’s an enterprise corollary to the work being done by startups like Wren, another Union Square Ventures investment, that focuses on offsetting the carbon footprint of everyday consumers. It’s also a competitor to companies like Pachama, which are trying to provide similar forest offsets at scale, or 3Degrees Inc. or South Pole.
Under a Biden administration there’s even more of an opportunity for these offset companies, the founders said, given discussions underway to establish a Carbon Bank. Established through the existing Commodity Credit Corp. run by the Department of Agriculture, the Carbon Bank would pay farmers and landowners across the U.S. for forestry and agricultural carbon offset projects.
“Everybody knows that there’s more value in these systems than just the product that we harvest off of it,” said Parisa. “Until we put those benefits in the same footing as the things we cut off and send to market…. As the value of these things goes up… absolutely it is going to influence these decisions and it is a cash crop… It’s a money pump from coastal America into middle America to create these things that they need.”
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How artificial intelligence will be used in 2021
Scale AI CEO Alexandr Wang doesn’t need a crystal ball to see where artificial intelligence will be used in the future. He just looks at his customer list.
The four-year-old startup, which recently hit a valuation of more than $3.5 billion, got its start supplying autonomous vehicle companies with the labeled data needed to train machine learning models to develop and eventually commercialize robotaxis, self-driving trucks and automated bots used in warehouses and on-demand delivery.
The wider adoption of AI across industries has been a bit of a slow burn over the past several years as company founders and executives begin to understand what the technology could do for their businesses.
In 2020, that changed as e-commerce, enterprise automation, government, insurance, real estate and robotics companies turned to Scale’s visual data labeling platform to develop and apply artificial intelligence to their respective businesses. Now, the company is preparing for the customer list to grow and become more varied.
How 2020 shaped up for AI
Scale AI’s customer list has included an array of autonomous vehicle companies including Alphabet, Voyage, nuTonomy, Embark, Nuro and Zoox. While it began to diversify with additions like Airbnb, DoorDash and Pinterest, there were still sectors that had yet to jump on board. That changed in 2020, Wang said.
Scale began to see incredible use cases of AI within the government as well as enterprise automation, according to Wang. Scale AI began working more closely with government agencies this year and added enterprise automation customers like States Title, a residential real estate company.
Wang also saw an increase in uses around conversational AI, in both consumer and enterprise applications as well as growth in e-commerce as companies sought out ways to use AI to provide personalized recommendations for its customers that were on par with Amazon.
Robotics continued to expand as well in 2020, although it spread to use cases beyond robotaxis, autonomous delivery and self-driving trucks, Wang said.
“A lot of the innovations that have happened within the self-driving industry, we’re starting to see trickle out throughout a lot of other robotics problems,” Wang said. “And so it’s been super exciting to see the breadth of AI continue to broaden and serve our ability to support all these use cases.”
The wider adoption of AI across industries has been a bit of a slow burn over the past several years as company founders and executives begin to understand what the technology could do for their businesses, Wang said, adding that advancements in natural language processing of text, improved offerings from cloud companies like AWS, Azure and Google Cloud and greater access to datasets helped sustain this trend.
“We’re finally getting to the point where we can help with computational AI, which has been this thing that’s been pitched for forever,” he said.
That slow burn heated up with the COVID-19 pandemic, said Wang, noting that interest has been particularly strong within government and enterprise automation as these entities looked for ways to operate more efficiently.
“There was this big reckoning,” Wang said of 2020 and the effect that COVID-19 had on traditional business enterprises.
If the future is mostly remote with consumers buying online instead of in-person, companies started to ask, “How do we start building for that?,” according to Wang.
The push for operational efficiency coupled with the capabilities of the technology is only going to accelerate the use of AI for automating processes like mortgage applications or customer loans at banks, Wang said, who noted that outside of the tech world there are industries that still rely on a lot of paper and manual processes.
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