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Four years ago, mathematician Vlad Voroninski saw an opportunity to remove some of the bottlenecks in the development of autonomous vehicle technology thanks to breakthroughs in deep learning.
Now, Helm.ai, the startup he co-founded in 2016 with Tudor Achim, is coming out of stealth with an announcement that it has raised $13 million in a seed round that includes investment from A.Capital Ventures, Amplo, Binnacle Partners, Sound Ventures, Fontinalis Partners and SV Angel. More than a dozen angel investors also participated, including Berggruen Holdings founder Nicolas Berggruen, Quora co-founders Charlie Cheever and Adam D’Angelo, professional NBA player Kevin Durant, Gen. David Petraeus, Matician co-founder and CEO Navneet Dalal, Quiet Capital managing partner Lee Linden and Robinhood co-founder Vladimir Tenev, among others.
Helm.ai will put the $13 million in seed funding toward advanced engineering and R&D and hiring more employees, as well as locking in and fulfilling deals with customers.
Helm.ai is focused solely on the software. It isn’t building the compute platform or sensors that are also required in a self-driving vehicle. Instead, it is agnostic to those variables. In the most basic terms, Helm.ai is creating software that tries to understand sensor data as well as a human would, in order to be able to drive, Voroninski said.
That aim doesn’t sound different from other companies. It’s Helm.ai’s approach to software that is noteworthy. Autonomous vehicle developers often rely on a combination of simulation and on-road testing, along with reams of data sets that have been annotated by humans, to train and improve the so-called “brain” of the self-driving vehicle.
Helm.ai says it has developed software that can skip those steps, which expedites the timeline and reduces costs. The startup uses an unsupervised learning approach to develop software that can train neural networks without the need for large-scale fleet data, simulation or annotation.
“There’s this very long tail end and an endless sea of corner cases to go through when developing AI software for autonomous vehicles, Voroninski explained. “What really matters is the unit of efficiency of how much does it cost to solve any given corner case, and how quickly can you do it? And so that’s the part that we really innovated on.”
Voroninski first became interested in autonomous driving at UCLA, where he learned about the technology from his undergrad adviser who had participated in the DARPA Grand Challenge, a driverless car competition in the U.S. funded by the Defense Advanced Research Projects Agency. And while Voroninski turned his attention to applied mathematics for the next decade — earning a PhD in math at UC Berkeley and then joining the faculty in the MIT mathematics department — he knew he’d eventually come back to autonomous vehicles.
By 2016, Voroninski said breakthroughs in deep learning created opportunities to jump in. Voroninski left MIT and Sift Security, a cybersecurity startup later acquired by Netskope, to start Helm.ai with Achim in November 2016.
“We identified some key challenges that we felt like weren’t being addressed with the traditional approaches,” Voroninski said. “We built some prototypes early on that made us believe that we can actually take this all the way.”
Helm.ai is still a small team of about 15 people. Its business aim is to license its software for two use cases — Level 2 (and a newer term called Level 2+) advanced driver assistance systems found in passenger vehicles and Level 4 autonomous vehicle fleets.
Helm.ai does have customers, some of which have gone beyond the pilot phase, Voroninski said, adding that he couldn’t name them.
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RFocus asks a simple question: What if instead of just antennas and transmitters on access points and mobile devices, we put the things just about everywhere? You know, just totally slather the walls with the stuff? The new “smart surface” from MIT’s CSAIL uses in excess of 3,000 antennas to boost signal strength by nearly 10x.
The department issued a paper today showcasing the technology, which is relatively cheap, with each antenna running a few cents. Better still, it’s low power, either reflecting a signal or allowing it through, depending on the software controller. CSAIL envisions a future where RFocus is used in homes and warehouses to boost signals for the Internet of Things and various network-connected devices.

“The core goal here was to explore whether we can use elements in the environment and arrange them to direct the signal in a way that we can actually control,” MIT professor Hari Balakrishnan said in a post detailing the technology. “If you want to have wireless devices that transmit at the lowest possible power, but give you a good signal, this seems to be one extremely promising way to do it.”
No word on a time frame to market here — that’s not really how CSAIL operates. The team also notes that similar research has been conducted by Princeton, though MIT’s focus is on low-cost and a wider range of applications. The notion of full-wall antennas certainly seems a bit far-fetched — and in most cases unnecessary. And given the sort of caution with which many have approached 5G, I suspect more research will have to be done on the long-term effects of such transitions.
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As the world grows increasingly digital, the craving for face-to-face connections is surging. Squad, an invite-only community and app, is trying to fill the need for offline connections by curating tight-knit events for Gen Z and Millennials.
“It mimics building relationships in real life,” says founder and CEO Isa Watson.
It’s an idea that investors are already backing: Squad closed a $3.5 million seed round and plans to raise its Series A in early 2020, but the road to securing that round was anything but easy. During a conversation on the How I Raised It podcast, Watson shared the ups and downs of her unique path to fundraising.
She started by putting some of the earliest capital into the business herself with support from her family. She then worked her way through more than 200 meetings in Silicon Valley to build up her credibility as a founder — a step that she can’t stress enough — before Squad even started its official seed round.
“Despite the fact that I went to MIT, despite the fact that I managed a billion-dollar product at JPMorgan Chase and even built a huge digital product, I was still a Silicon Valley outsider,” Watson says.
People sometimes have the perception that being an alumni at a top U.S. university will mean they can go to Silicon Valley and just be “in,” Watson explains, but that’s not quite how it works.
“It takes a lot of work and a lot of credibility building,” she says. “That’s what I was doing for a few years before we actually did our official seed round. By the time I did it, it was like my reputation preceded me and there was enough familiarity with me.”
Isa Watson, Squad founder and CEO
Despite taking more than 200 meetings in her efforts to crack Silicon Valley, Watson never took a cold meeting.
“Cold outreach is a tactic that I see a lot of founders using,” she says, “whereas I would argue that the more effective introduction comes from someone who knows someone.”
Leveraging the connections she built was critical in connecting Watson to her eventual funders. “They’re all referring you to the next three people to talk to,” Watson says. “It becomes like tree branches and then a network that’s growing in a multiplicative fashion.”
One of Squad’s earliest investors was Steven Aldrich, who at the time was working as chief product officer at GoDaddy . Both Aldrich and Watson grew up in North Carolina, and Steven’s father shared hometown roots with her, which helped her make the initial connection.
“It was about consistently making connections like that,” she says. “Steven introduced me to three people, and then those three other people introduced me to two people. And that’s essentially how I got the ball rolling.”
Not all meetings need to be about meeting for coffees or lunches, either — Watson took plenty of calls while expanding her network, as well. But the important step was making those connections, which was “a really hard hustle and grind, head down,” for the first two years.
When meeting people in Silicon Valley or expanding her network of prospective funders, Watson didn’t tease future funding rounds or send off vague meeting requests.
In trying to build out her network, she first researched a couple of key things: who did she need to know in order to build a really strong product, and who did she need to know in order to have solid distribution or growth marketing? Once she identified those folks, she would reach out to them individually and ask them for specific advice in their area of expertise.
“People always say, ‘When you want money, ask for advice. If you want advice, ask for money,’” Watson says. “Being super-explicit in the ask and explaining how you’ll spend their time and their brain space is super important.” No one has time for a generic request like, “Hey, can I pick your brain?”
When you’ve connected with someone, you should always ask them for recommendations for experts in specific areas — like growth marketing, product, etc. If they volunteer a few names, ask if you can send an email that they could forward on to introduce you to those individuals.
Following the introductions, it’s important to remember that it’s not just a “one and done,” as she says. Once you’ve met with someone through an introduction, follow up: let them know how the meetings went and thank them again.
“It’s like really, really intense relationship management, and it’s something that people with the highest EQ do best,” says Watson. “I would identify my needs, make specific asks … and then I would make sure to explicitly ask if they did not offer for three other intros for people that could be helpful, that would be excited about what we’re doing.”
When she realized it was time to start raising money for Squad, her first move was to identify her “quarterback for fundraising” — in this case, Charles Hudson from Precursor Ventures. It’s helpful, according to Watson, to not have “too many cooks in the kitchen,” or else you’ll end up with far too many opinions that don’t align.
Hudson had already invested a small amount of money in Squad at the time, but he quickly became the person Watson went to for feedback on her pitches. He counseled her on other aspects of running a process.
“One thing Charles tells me is that, with fundraising, you’re likely only going to be successful if that’s your core focus at that time,” Watson says. “It’s not something you can do passively.”
So Hudson and Watson sat down and came up with a list of 35 target venture capitalists. He introduced her to five who she didn’t expect to be a good fit. They first went with the ones they didn’t expect would be a perfect match so she could gather feedback and see if Squad was actually ready to raise capital.
Of those first five meetings, one or two “were complete dings” and turned Squad down outright — but Watson made it to partner meetings in the three other meetings, a sign that VCs were seriously considering Squad.
Based on that feedback, Hudson introduced Watson to 10 more VCs — and shortly after, she met Michael Dearing at Harrison Metal, who led Squad’s seed round.
After Dearing offered up a term sheet of $3 million, Watson quickly had offers from other VCs.
“It’s funny because it took me deliberately being in the market for fundraising for like two and a half months to get that ‘yes’ from Michael. Before that, I had no cash really committed,” she says. “And then after just a few days of letting people know I had a term sheet for $3 million, I had like $6 million on a table. VCs are such followers.”
With that many offers on the table following Dearing’s lead, Watson was in the enviable position of needing to pick who she’d let into the seed round. So how did she choose?
“The first thing is value add,” Watson says. She asked herself: “did I feel like I had the right assortment of value? I maybe want someone in there who’s really strong on product; I may want someone who’s really strong at growth, strong at marketing.”
Her second criteria for making the decision was a less resume-focused. Simply put, she went with her gut.
“One thing that founders really, really underestimate is — is this person a good human being? I went with the people that I had felt most comfortable with, the people who I felt I could trust based on my interactions with them, and who were just supportive along the way.”
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Neural Magic, a startup founded by a couple of MIT professors, who figured out a way to run machine learning models on commodity CPUs, announced a $15 million seed investment today.
Comcast Ventures led the round, with participation from NEA, Andreessen Horowitz, Pillar VC and Amdocs. The company had previously received a $5 million pre-seed, making the total raised so far $20 million.
The company also announced early access to its first product, an inference engine that data scientists can run on computers running CPUs, rather than specialized chips like GPUs or TPUs. That means that it could greatly reduce the cost associated with machine learning projects by allowing data scientists to use commodity hardware.
The idea for this solution came from work by MIT professor Nir Shavit and his research partner and co-founder Alex Mateev. As he tells it, they were working on neurobiology data in their lab and found a way to use the commodity hardware he had in place. “I discovered that with the right algorithms we could run these machine learning algorithms on commodity hardware, and that’s where the company started,” Shavit told TechCrunch.
He says there is this false notion that you need these specialized chips or hardware accelerators to have the necessary resources to run these jobs, but he says it doesn’t have to be that way. He says his company not only allows you to use this commodity hardware, it also works with more modern development approaches, like containers and microservices.
“Our vision is to enable data science teams to take advantage of the ubiquitous computing platforms they already own to run deep learning models at GPU speeds — in a flexible and containerized way that only commodity CPUs can deliver,” Shavit explained.
He says this also eliminates the memory limitations of these other approaches because CPUs have access to much greater amounts of memory, and this is a key advantage of his company’s approach over and above the cost savings.
“Yes, running on a commodity processor you get the cost savings of running on a CPU, but more importantly, it eliminates all of these huge commercialization problems and essentially this big limitation of the whole field of machine learning of having to work on small models and small data sets because the accelerators are kind of limited. This is the big unlock of Neural Magic,” he said.
Gil Beyda, managing director at lead investor Comcast Ventures, sees a huge market opportunity with an approach that lets people use commodity hardware. “Neural Magic is well down the path of using software to replace high-cost, specialized AI hardware. Software wins because it unlocks the true potential of deep learning to build novel applications and address some of the industry’s biggest challenges,” he said in a statement.
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The Massachusetts Institute of Technology said it is reviewing the university’s relationship with SenseTime, one of eight Chinese tech companies placed on the U.S. Entity List yesterday for their alleged role in human rights abuses against Muslim minority groups in China.
An MIT spokesperson told Bloomberg that “MIT has long had a robust export controls function that pays careful attention to export control regulations and compliance. MIT will review all existing relationships with organizations added to the U.S. Department of Commerce’s Entity List, and modify any interactions, as necessary.”
A SenseTime representative told Bloomberg, “We are deeply disappointed with this decision by the U.S. Department of Commerce. We will work closely with all relevant authorities to fully understand and resolve the situation.”
The companies placed on the blacklist included several of China’s top AI startups and companies that have supplied software to mass surveillance systems that may have been used by the Chinese government to persecute Uighurs and other Muslim minority groups.
Over one million Uighurs are believed to currently be held in detention camps, where human rights observers report they have been subjected to forced labor and torture.
SenseTime, the world’s mostly highly valued AI startup, provided software to the Chinese government for its national surveillance system, including CCTV cameras. It was the first company to join an MIT Intelligence Quest initiative launched last year with the goal of “driv[ing] technological breakthroughs in AI that have the potential to confront some of the world’s greatest challenges.” Since then, it has provided funding for 27 projects by MIT researchers.
Earlier this year, MIT ended its working relationships with Huawei and ZTE over alleged sanction violations.
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Much of Silicon Valley mythology is centered on the founder-as-hero narrative. But historically, scientific founders leading the charge for bio companies have been far less common.
Developing new drugs is slow, risky and expensive. Big clinical failures are all too common. As such, bio requires incredibly specialized knowledge and experience. But at the same time, the potential for value creation is enormous today more than ever with breakthrough new medicines like engineered cell, gene and digital therapies.
What these breakthroughs are bringing along with them are entirely new models — of founders, of company creation, of the businesses themselves — that will require scientists, entrepreneurs and investors to reimagine and reinvent how they create bio companies.
In the past, biotech VC firms handled this combination of specialized knowledge + binary risk + outsized opportunity with a unique “company creation” model. In this model, there are scientific founders, yes; but the VC firm essentially founded and built the company itself — all the way from matching a scientific advance with an unmet medical need, to licensing IP, to having partners take on key roles such as CEO in the early stages, to then recruiting a seasoned management team to execute on the vision.
Image: PASIEKA/SCIENCE PHOTO LIBRARY/Getty Images
You could call this the startup equivalent of being born and bred in captivity — where great care and feeding early in life helps ensure that the company is able to thrive. Here the scientific founders tend to play more of an advisory role (usually keeping day jobs in academia to create new knowledge and frontiers), while experienced “drug hunters” operate the machinery of bringing new discoveries to the patient’s bedside. This model’s core purpose is to bring the right expertise to the table to de-risk these incredibly challenging enterprises — nobody is born knowing how to make a medicine.
But the ecosystem this model evolved from is evolving itself. Emerging fields like computational biology and biological engineering have created a new breed of founder, native to biology, engineering and computer science, that are already, by definition, the leading experts in their fledgling fields. Their advances are helping change the industry, shifting drug discovery away from a highly bespoke process — where little knowledge carries over from the success or failure of one drug to the next — to a more iterative, building-block approach like engineering.
Take gene therapy: once we learn how to deliver a gene to a specific cell in a given disease, it is significantly more likely we will be able to deliver a different gene to a different cell for another disease. Which means there’s an opportunity not only for novel therapies but also the potential for new business models. Imagine a company that provides gene delivery capability to an entire industry — GaaS: gene-delivery as a service!
Once a founder has an idea, the costs of testing it out have changed too. The days of having to set up an entire lab before you could run your first experiments are gone. In the same way that AWS made starting a tech company vastly faster and easier, innovations like shared lab spaces and wetlab accelerators have dramatically reduced the cost and speed required to get a bio startup off the ground. Today it costs thousands, not millions, for a “killer experiment” that will give a founding team (and investors) early conviction.
What all this amounts to is scientific founders now have the option of launching bio companies without relying on VCs to create them on their behalf. And many are. The new generation of bio companies being launched by these founders are more akin to being born in the wild. It isn’t easy; in fact, it’s a jungle out there, so you need to make mistakes, learn quickly, hone your instincts, and be well-equipped for survival. On the other hand, given the transformative potential of engineering-based bio platforms, the cubs that do survive can grow into lions.
Image via Getty Images / KTSDESIGN/SCIENCE PHOTO LIBRARY
So, which is better for a bio startup today: to be born in the wild — with all the risk and reward that entails — or to be raised in captivity
The “bred in captivity” model promises sureness, safety, security. A VC-created bio company has cache and credibility right off the bat. Launch capital is essentially guaranteed. It attracts all-star scientists, executives and advisors — drawn by the balance of an innovative, agile environment and a well-funded, well-connected support network. I was fortunate enough to be an early executive in one of these companies, giving me the opportunity to work alongside industry luminaries and benefit from their well-versed knowledge of how to build a world-class bio company with all its complex component parts: basic, translational, clinical research, from scratch. But this all comes at a price.
Because it’s a heavy lift for the VCs, scientific founders are usually left with a relatively small slug of equity — even founding CEOs can end up with ~5% ownership. While these companies often launch with headline-grabbing funding rounds of $50m or above, the capital is tranched — meaning money is doled out as planned milestones are achieved. But the problem is, things rarely go according to plan. Tranched capital can be a safety net, but you can get tangled in that net if you miss a milestone.
Being born in the wild, on the other hand, trades safety for freedom. No one is building the company on your behalf; you’re in charge, and you bear the risk. As a recent graduate, I co-founded a company with Harvard geneticist George Church. The company was bootstrapped — a funding strategy that was more famine than feast — but we were at liberty to try new things and run (un)controlled experiments like sequencing heavy metal wildman Ozzy Osbourne.

It was the early, Wild West days of the genomics revolution and many of the earliest biotech companies mirrored that experience — they weren’t incepted by VCs; they were created by scrappy entrepreneurs and scientists-turned-CEO. Take Joshua Boger, organic chemist and founder of Vertex Pharmaceuticals: starting in 1989 his efforts to will into existence a new way to develop drugs, thrillingly captured in Barry Werth’s The Billion-Dollar Molecule and its sequel The Antidote in all its warts and nail-biting glory, ultimately transformed how we treat HIV, hepatitis C and cystic fibrosis.
Today we’re in a back-to-the-future moment and the industry is being increasingly pushed forward by this new breed of scientist-entrepreneur. Students-turned-founder like Diego Rey of in vitro diagnostics company GeneWEAVE and Ramji Srinivasan of clinical laboratory Counsyl helped transform how we diagnose disease and each led their companies to successful acquisitions by larger rivals.
Popular accelerators like Y Combinator and IndieBio are filled with bio companies driven by this founder phenotype. Ginkgo Bioworks, the first bio company in Y Combinator and today a unicorn, was founded by Jason Kelly and three of his MIT biological engineering classmates, along with former MIT professor and synthetic biology legend Tom Knight. The company is not only innovating new ways to program biology in order to disrupt a broad range of industries, but it’s also pioneering an innovative conglomerate business model it has dubbed the “Berkshire for biotech.”
Like the Ginkgo founders, Alec Nielsen and Raja Srinivas launched their startup Asimov, an ambitious effort to program cells using genetic circuits, shortly after receiving their PhDs in biological engineering from MIT. And, like Boger, renowned machine learning Stanford professor Daphne Koller is working to once again transform drug discovery as the founder and CEO of Instiro.
Just like making a medicine, no one is born knowing how to build a company. But in this new world, these technical founders with deep domain expertise may even be more capable of traversing the idea maze than seasoned operators. Engineering-based platforms have the potential to create entirely new applications with unprecedented productivity, creating opportunities for new breakthroughs, novel business models, and new ways to build bio companies. The well-worn playbooks may be out of date.
Founders that choose to create their own companies still need investors to scrub in and contribute to the arduous labor of company-building — but via support, guidance, and with access to networks instead. And like this new generation of founders, bio investors today need to rethink (and re-value) the promise of the new, and still appreciate the hard-earned wisdom of the old. In other words, bio investors also need to be multidisciplinary. And they need to be comfortable with a different kind of risk: backing an unproven founder in a new, emerging space. As a founder, if you’re willing to take your chances in the wild, you should have an investor that understands you, believes in you, can support you and, importantly, is willing to dream big with you.
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There are few topics as hot right now in the enterprise as customer experience management, that ability to collect detailed data about your customers, then deliver customized experiences based on what you have learned about them. To help understand the challenges companies face building this kind of experience, we are bringing Segment CEO Peter Reinhardt to TechCrunch Sessions: Enterprise on September 5 in San Francisco (p.s. early-bird sales end this Friday, August 9).
At the root of customer experience management is data — tons and tons of data. It may come from the customer journey through a website or app, basic information you know about the customer or the customer’s transaction history. It’s hundreds of signals and collecting that data in order to build the experience where Reinhardt’s company comes in.
Segment wants to provide the infrastructure to collect and understand all of that data. Once you have that in place, you can build data models and then develop applications that make use of the data to drive a better experience.
Reinhardt, and a panel that includes Qualtrics’ Julie Larson-Green and Adobe’s Amit Ahuja, will discuss with TechCrunch editors the difficulties companies face collecting all of that data to build a picture of the customer, then using it to deliver more meaningful experiences for them. See the full agenda here.
Segment was born in the proverbial dorm room at MIT when Reinhardt and his co-founders were students there. They have raised more than $280 million since inception. Customers include Atlassian, Bonobos, Instacart, Levis and Intuit .
Early-bird tickets to see Peter and our lineup of enterprise influencers at TC Sessions: Enterprise are on sale for just $249 when you book here; but hurry, prices go up by $100 after this Friday!
Are you an early-stage startup in the enterprise-tech space? Book a demo table for $2,000 and get in front of TechCrunch editors and future customers/investors. Each demo table comes with four tickets to enjoy the show.
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Electric-vehicle chargers today are designed for human drivers. Electrify America and San Francisco-based startup Stable are preparing for the day when humans are no longer behind the wheel.
Electrify America, the entity set up by Volkswagen as part of its settlement with U.S. regulators over the diesel emissions cheating scandal, is partnering with Stable to test a system that can charge electric vehicles without human intervention.
The autonomous electric-vehicle charging system will combine Electrify America’s 150 kilowatt DC fast charger with Stable’s software and robotics. A robotic arm, which is equipped with computer vision to see the electric vehicle’s charging port, is attached to the EV charger. The two companies plan to open the autonomous charging site in San Francisco by early 2020.
There’s more to this system than a nifty robotic arm. Stable’s software and modeling algorithms are critical components that have applications today, not just the yet-to-be-determined era of ubiquitous robotaxis.
While streets today aren’t flooded with autonomous vehicles, they are filled with thousands of vehicles used by corporate and government fleets, as well as ride-hailing platforms like Uber and Lyft . Those commercial-focused vehicles are increasingly electric, a shift driven by economics and regulations.
“For the first time these fleets are having to think about, ‘how are we going to charge these massive fleets of electric vehicles, whether they are autonomous or not?’ ” Stable co-founder and CEO Rohan Puri told TechCrunch in a recent interview.
Stable, a 10-person company with employees from Tesla, EVgo, Faraday Future, Google, Stanford and MIT universities, has developed data science algorithms to determine the best location for chargers and scheduling software for once the EV stations are deployed.
Its data science algorithms take into account installation costs, available power, real estate costs as well as travel time for the given vehicle to go to the site and then get back on the road to service customers. Stable has figured out that when it comes to commercial fleets, chargers in a distributed network within cities are used more and have a lower cost of operation than one giant centralized charging hub.
Once a site is deployed, Stable’s software directs when, how long and at what speed the electric vehicle should charge.
Stable, which launched in 2017, is backed by Trucks VC, Upside Partnership, MIT’s E14 Fund and a number of angel investors, including NerdWallet co-founder Jake Gibson and Sidecar co-founder and CEO Sunil Paul .
The pilot project in San Francisco is the start of what Puri hopes will lead to more fleet-focused sites with Electrify America, which has largely focused on consumer charging stations. Electrify America has said it will invest $2 billion over 10 years in clean energy infrastructure and education. The VW unit has more than 486 electric vehicle charging stations installed or under development. Of those, 262 charging stations have been commissioned and are now open to the public.
Meanwhile, Stable is keen to demonstrate its autonomous electric-vehicle chargers and lock in additional fleet customers.
“What we set out to do was to reinvent the gas station for this new era of transportation, which will be fleet-dominant and electric,” Puri said. “What’s clear is there just isn’t nearly enough of the right infrastructure installed in the right place.”
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Jumia may be the first startup you’ve heard of from Africa. But the e-commerce venture that recently listed on the NYSE is definitely not the first or last word in African tech.
The continent has an expansive digital innovation scene, the components of which are intersecting rapidly across Africa’s 54 countries and 1.2 billion people.
When measured by monetary values, Africa’s tech ecosystem is tiny by Shenzen or Silicon Valley standards.
But when you look at volumes and year over year expansion in VC, startup formation, and tech hubs, it’s one of the fastest growing tech markets in the world. In 2017, the continent also saw the largest global increase in internet users—20 percent.
If you’re a VC or founder in London, Bangalore, or San Francisco, you’ll likely interact with some part of Africa’s tech landscape for the first time—or more—in the near future.
That’s why TechCrunch put together this Extra-Crunch deep-dive on Africa’s technology sector.
A foundation for African tech is the continent’s 442 active hubs, accelerators, and incubators (as tallied by GSMA). These spaces have become focal points for startup formation, digital skills building, events, and IT activity on the continent.
Prominent tech hubs in Africa include CcHub in Nigeria, Pan-African incubator MEST, and Kenya’s iHub, with over 200 resident members. More of these organizations are receiving funds from DFIs, such as the World Bank, and aid agencies, including France’s $76 million African tech fund.
Blue-chip companies such as Google and Microsoft are also providing money and support. In 2018 Facebook opened its own Hub_NG in Lagos with partner CcHub, to foster startups using AI and machine learning.
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The enterprise software and services-focused accelerator Alchemist has raised $4 million in fresh financing from investors BASF and the Qatar Development Bank, just in time for its latest demo day unveiling 20 new companies.
Qatar and BASF join previous investors, including the venture firms Mayfield, Khosla Ventures, Foundation Capital, DFJ and USVP, and corporate investors like Cisco, Siemens and Juniper Networks.
While the roster of successes from Alchemist’s fund isn’t as lengthy as Y Combinator, the accelerator program has launched the likes of the quantum computing upstart Rigetti, the soft-launch developer tool LaunchDarkly and drone startup Matternet .
Some (personal) highlights of the latest cohort include:
Watch a live stream of Alchemist’s demo day pitches, starting at 3PM, here.
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