Charlie Cheever
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Sohail Prasad and Samvit Ramadurgam are cofounders who met during Y Combinator’s 2012 summer batch and went on to cofound Forge, which helps accredited investors and institutions buy and sell private company shares and which most recently raised $150 million in new funding in May.
Forge — originally known as Equidate — has taken off as demand for private company shares has ballooned. The company, launched in 2014, has now raised $250 million altogether, including from, Deutsche Börse, Temasek, Wells Fargo, BNP Paribas, and Munich Re. It acquired rival SharesPost last year for $160 million in cash and stock. According to the company, it now has more than $14 billion in assets under custody.
Prasad and Ramadurgam — who helped hire Forge CEO Kelly Rodriques back in 2018 — say they’re excited about that success. They still own a stake in the company; they remain non-voting board members.
But after spending 18 months as co-president of Forge at the outset of Rodrigues’s tenure, they left early last year to begin tinkering on a new idea, one that Prasad says is centered around giving a much wider pool of people access to private company shares. Called D/XYZ (pronounced “Destiny”), the idea is to enable any investor — not just the 1% — to invest in startups whose services they use and love.
Unfortunately, the two aren’t offering much more of a curtain raiser than that right now, though Prasad suggests D/XYZ is neither a new fund nor a crowdfunding vehicle. It’s also not selling any tokens, we gather. Instead, Prasad hints at an entirely new product, saying the company is being cautious in how much it shares publicly because it first wants to “get the go-ahead from regulators, as well as to ensure we have a clear path to market,” he says.
In the meantime, the two have raised $5 million in seed funding from numerous founders who like the idea of making private company shares easier for their parents, friends, customers, partners, and everyone else who likes what they’re building. Among the round’s participants is Coinbase cofounder Fred Ehrsam; Plaid cofounder and CEO Zach Perret; Quora and Expo cofounder Charlie Cheever; Superhuman founder and CEO Rahul Vohra; and serial entrepreneur Siqi Chen, who most recently founded a finance software company called Runway.
As for some of the nascent startup’s most obvious competition, Prasad doesn’t sound concerned. Asked, for example, about Carta, a well-funded company that helps private companies and their employees manage and sell their stock and options and that has long talked about democratizing access to private company shares, Prasad says it remains very much a direct competitor instead to Forge given that both cater first and foremost to companies, not individuals.
And what of SPACs, the special purpose acquisition companies that are moving private companies onto the public market faster, allowing (at least in theory) more people to access high-growth companies at earlier stages? It’s a partial solution, says Prasad. But the way he sees it, “SPACs are more a reflection that people want late-stage access to private tech and their best option right now is giving money to a SPAC manager who will hopefully find a promising company to merge with in two years or less.” He calls them a “layer of abstraction.”
Of course, there’s also the question of whether Forge will be a friend of foe if whatever Prasad and Ramadurgam are building succeeds. Could their tech be sold back to their first company? Could Forge come to see them as a rival to its business?
“What we’re doing now is not competitive,” insists Prasad. “It’s more picking up the mantle where we left off. Forge is focused on trading, custody, company solutions and data. It has built what some call boring plumbing.” Now that the plumbing has been erected, it has “enabled a lot of other interesting things to be built, too.”
So is D/XYZ working with Forge in some capacity? Prasad demurs. “Potentially,” he says.
In other words, stay tuned.
Pictured above, left to right: Sohail Prasad and Samvit Ramadurgam.
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Workplace SaaS tools for teams have seen rocket ship growth in the past several years, and that adoption has given rise to a host of software tools geared toward improving individual productivity. Many of the startups behind these tools see building a cult following among individual users as the best way to set themselves up for later enterprise-wide success.
Raycast is a developer-focused productivity tool that aims to be the quickest way to get common tasks done. Today, it’s launching into public beta and sharing with TechCrunch that the team has raised new funding from Accel months after graduating from Y Combinator.
The company has closed a $2.7 million seed round led by Accel, with participation from YC, Jeff Morris Jr.’s Chapter One fund, as well as angel investors Charlie Cheever, Calvin French-Owen and Manik Gupta .
The desktop software takes a note from peers like Superhuman and Command E, allowing users to quickly pull up and modify data with keyboard shortcuts. Users can easily create and re-modify issues in Jira, merge pull requests in GitHub and find documents. The software is very much a developer-focused version of Apple’s Spotlight search that aims to help software engineers navigate with a single tool all the parts of their job that aren’t development work.
Image via Raycast.
Like plenty of workplace tools startups, one of the keys for Raycast is building out a network of extensions that can encompass a user’s workflow. For now, the software supports integrations from Asana, Jira, Zoom, Linear, G Suite, Calendar, GitHub and Reminders, alongside core functionality that can help manage system settings and a calculator that can handle complex math problems. As the startup launches out of public beta, they’re looking to double down on extensions and are rolling out a developer program for early access to their API.
The Mac-only software is free while in public beta, but the company does plan on charging a monthly subscription for the service eventually, though they aren’t quite ready to talk about pricing yet.
Raycast’s team is interested in appealing to individual users for now, but might eventually expand to becoming a teams-level enterprise product that could help onboard new employees faster by quickly orienting them with their office’s software suite, but that’s all a bit down the road, the team says.
“We’re staying focused on single-player mode for a while,” CEO Thomas Paul Mann tells TechCrunch.
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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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