All posts in “Venture Capital”

Watch the first four episodes of the Silicon Valley mockumentary ‘Bubbleproof’

We’re excited to be premiering a new mockumentary series about the startup world; watch the first four episodes of Bubbleproof right here on TechCrunch.

The show stars founder Michael Fertik and David Cowan of Bessemer Venture Partners. Fertik and Cowan play fictionalized (?) versions of themselves — the successful entrepreneur who becomes even more insufferable when he takes charge of a $200 million fund, and the venture capitalist who’s eager to share in the spotlight.

Fertik and Cowan also co-wrote the series (which is a follow-up to the short films Femto-Management: A Micromentary and CI: A TEDD Talkumentary) and they took the stage at Disrupt SF to premiere the first episode.

“It’s about time that we make more fun of ourselves in Silicon Valley,” Fertik said.

Sure, there’s already HBO’s Silicon Valley, which does plenty of research in the real-world Silicon Valley (and has already put some of the industry’s more notable figures on-screen). But Bubbleproof goes even further in blurring the lines between reality and satire. In fact, the show’s crew was shooting backstage at Disrupt as its creators — in character as the Bubbleproof versions of themselves — prepared to go onstage.

Watch the trailer and the first four episodes of the series below.


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Episode 1

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Episode 2

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Episode 3

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Episode 4

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MIT’s The Engine wants to fuel bold tech ideas in Boston

Boston and its surrounding universities are jam-packed with big ideas, but the problem is that many of them never get out of the lab. MIT president Rafael Reif recognized this and decided the city needed an engine to push those ideas and The Engine — part venture capital firm, part business incubator — was born.

When smart people are working on hard problems inside a lab, they have access to all of the resources of the university including all that expensive lab equipment and faculty brain power. Once they leave academia, it can be hard to get access to either one, especially when the equipment alone could cost hundreds of thousands of dollars (or more).

The other issue is the problem every smart person with a good idea and no business experience faces. How do you translate that idea into a repeatable business that solves a real-world problem? “While these people tend to be incredibly smart and experts in their field, they [often] don’t know how to run a company. They need support and mentorship and they need access to industry partners to prototype their ideas,” Fran Barros, design director at The Engine told me in a recent meeting in their Cambridge headquarters.

The Engine was launched with a $200 million fund in September to help solve this challenge.

They are looking for big, bold ideas in science and engineering that have the potential to benefit society in some way, and that might have trouble getting off the ground otherwise. “We have a mission for impact in the world, not just cool technology, but something that addresses a societal need and has great impact and the drive to be a big ambitious company,” Ally Yost, an associate at The Engine explained.

Like any VC out there, the company is trying to find a promising team with a crazy good idea. Among the areas they are exploring include advanced manufacturing, robotics, space, energy, life sciences and biotech. And they especially like to see multi-disciplinary ideas that move across these broader categories.

The Engine is looking for startups based in Boston or who are willing to relocate. The company itself offers a startup space with equipment for those who need it. Then there is the matter of being in Boston, which as Barros points out, is a city full of experts that can act as an external network for the company’s startups to fill in knowledge gaps.

Among the ideas in the first batch is C2Sense, a company that has developed a digital sense of smell of sorts that could help food companies detect gasses that signal when their food is going to spoil before it happens, and Analytical Space, a company run by two former White House employees involved in space research, who want to develop a more efficient way to move lots of data being collected by satellites to storage on earth, a problem that is hard to solve right now.

The Engine has a broad vision for the company as a concept. The first batch consists of seven companies, but they expect to fund between 50 and 60 before they are said and done with this round of funding. Eventually, they hope to spawn Engines in other cities throughout the world and spread the concept.

LimeBike raises $50M to further its bike-sharing ambitions

LimeBike, one of several companies competing in the rapidly expanding bike-sharing space, has raised a $50 million B round to continue building out the operation. The company is also looking into ways to differentiate itself from the competition — including the two 900-pound Chinese gorillas in the industry, Ofo and Mobike.

I chatted with LimeBike’s CEO, Toby Sun, about the company’s ambitions and rivals. He felt LimeBike has an advantage over its rich Chinese rivals in that it’s the leading U.S. company in a market that, in the U.S. at least, is still young.

That’s not to say he’s banking on consumers’ patriotism to do the work for him. LimeBike, he explained, is aiming at being a good partner with cities and schools, tailoring their bikes and programs to fit the needs of different locations.

“We take a different approach to every market we enter,” he said. “We talk for weeks, sometimes months, to municipalities and schools, which are the primary markets for us, and customize the deployment and plans for them.”

Operating a floating bike-share service can’t be done surreptitiously, unlike for example soft-launching a service like Uber in a city that may or may not support it. So, Sun said, the better approach is to be a good long-term partner. It certainly adds complexity to the operation to have slightly different rules or abide by different requirements for different locations, but governments and companies will remember this helpful accommodation.

The money will go towards breaking into new markets — Sun said he’d like to hit at least 30 locations in the near future and to outpace station-based bike-share systems. It’ll also fund the R&D needed to keep up with competitors and the market-resident operations departments that service and “rebalance” bikes (read: drive from the bottoms of hills to the tops).

LimeBike raised $12 million in March — but unlike software startups, the costs of expanding the market of a bike-share service are considerable. Plus keeping 10,000 bikes in good condition. So it’s no surprise that they burned through that.

One feature Sun mentioned that I haven’t seen in the U.S. market yet is incentivizing users to do the rebalancing themselves. Giving someone a free ride if they go out of their way to take an isolated bike seems to me a no-brainer feature to add, but it’s probably harder than I think to get it right.

The recent emergence of services that aggregate bike locations from multiple services prompted me to ask if LimeBike would be officially supporting such apps, perhaps with the ability to pay through them.

“We’re still carefully evaluating this approach,” he said. The apps in question are using the data more or less without LimeBike’s permission, but they’re not about to try to stop them — “we want to see how it goes. It’s a question of priority and user needs.”

The funding round was led by Coatue (partner Thomas Laffont will be joining the board), with A16Z, DCM, GGV, Section 32, Yuri Milner, The Durant Comapny, and a few others joining.

Featured Image: LimeBike

ROSS Intelligence lands $8.7M Series A to speed up legal research with AI

Armed with an understanding of machine learning, ROSS Intelligence is going after LexisNexis and Thomson Reuters for ownership of legal research. The startup, founded in 2015 by Andrew Arruda, Jimoh Ovbiagele and Pargles Dall’Oglio at the University of Toronto, is announcing an $8.7 million Series A today led by iNovia Capital with participation from Comcast Ventures Catalyst Fund, Y Combinator Continuity Fund, Real Ventures, Dentons’ NextLaw Labs and angels.

At its core, ROSS is a platform that helps legal teams sort through case law to find details relevant to new cases. This process takes days and even weeks with standard keyword search, so ROSS is augmenting keyword search with machine learning to simultaneously speed up the research process and improve relevancy of items found.

“Bluehill benchmarks Lexis’s tech and they are finding 30 percent more relevant info with ROSS in less time,” Andrew Arruda, co-founder and CEO of ROSS, explained to me in an interview.

ROSS is using a combination of off the shelf and proprietary deep learning algorithms for its AI stack. The startup is using IBM Watson for at least some of its natural language processing capabilities, but the team shied away from elaborating.

Building a complete machine learning stack is expensive, so it makes sense for startups to lean on off the shelf tech early on so long as decisions are being made that ensure the scalability of the business. Much of the value wrapped up in ROSS is related to its corpus of training data. The startup is working with 20 law firms to simulate workflow examples and test results with human feedback.

“We really spent time looking at the value ROSS was delivering back to law firms,” noted Kai Bond, an investor in ROSS through Comcast Ventures. “What took a week now takes two to four hours.”

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The company’s initial plan to get to market was to sell software designed for specific domains of law to large firms like Latham & Watkins and Sidley Austin. Today ROSS offers products in both bankruptcy and intellectual property law. It is looking to expand into other types of law, like labor and employment, simultaneously moving down to serve smaller firms.

LexisNexis and Thomson Reuters are frequently on the butt end of claims made by machine learning-powered data analytics startups emerging in a potpourri of industries. A strategy favored by many of these businesses is pushing products to interns and college students for free so that they, in turn, push their advanced tools into the arms of future employers.

“The work ROSS is doing with law schools and law students is interesting,” Karam Nijjar, a partner at iNovia Capital and investor in ROSS, asserted. “As these students enter the workforce, you’re taking someone using an iPhone and handing them a BlackBerry their first day on the job.”

Prior to today’s Series A, ROSS had secured a $4.3 million seed round also led by iNovia Capital. As ROSS moves to scale it will be navigating a heavy field of mergers and acquisitions and attempts by legacy players to ensure legal tech services remain consolidated.

Petuum secures $93M Series B to push AI into the mainstream

With a shortage of machine learning developers bearing down on the industry, startups and big tech companies alike are moving to democratize the tools necessary to commercialize artificial intelligence. The latest startup, Petuum, is announcing a $93 million Series B this morning from Softbank and Advantech Capital.

Founded last year by Dr. Eric Xing, a Carnegie Mellon machine learning professor, Dr. Qirong Ho and Dr. Ning Li, Petuum is building software to facilitate two components of machine learning development. First, the team is automating aspects of data preparation and machine learning model selection. This is useful for novices that might otherwise struggle to even make use of common machine learning frameworks like TensorFlow and Caffe.

Once models have been selected, Petuum can also assist developers in optimizing for specific hardware constraints. This means virtualizing hardware to remove barriers — taking out the extra step of managing a distributed GPU cluster.

“The way we treat AI is not as an artisanal craft,” Dr. Xing explained to me in an interview. “We are trying to create very standardized building blocks that can be assembled and reassembled like legos.”

Petuum founder Dr. Eric Xing inside the startup’s offices in Pittsburgh

The point here isn’t to solve every problem in machine learning, but rather to automate enough of the process that industry can move from 0 to 1. That said, Petuum is attempting to build for both the expert and the novice — a tough balance to strike.

“Everyone knows how to use Excel,” asserted Dr. Xing. “A layman can use Excel to create a table. A highly skilled statistician modeling certain phenomenons can still use Excel.”

The other challenge facing Petuum is one of market strategy. As the tech industry grapples with its dumb money in AI problem, many investors have turned to heuristics to manage uncertainty — most popular of which is that horizontal platform AI plays don’t work.

The concern is that it’s difficult to outgun Google and Amazon in the machine learning-as-a-service space as a startup that needs to balance feature development and spending. Dr. Xing deferred to the skill of his team and while he didn’t directly mention it — the goldmine from Softbank won’t hurt. This is something that others like and Algorithmia can’t claim to date.

To the company’s credit, it is starting by going after healthcare and fintech customers. Though in the long run, Petuum doesn’t intend to cover every vertical. Petuum is working with beta testers in different industries so that in the future, outsiders can develop and deploy solutions on top of the platform.

Today’s investment comes from Softbank proper rather than the $93 billion Softbank Vision Fund. It’s unclear whether Softbank intends to shift the investment into the fund in the future. Petuum currently claims 70 employees and says that it will be expanding simultaneously in product, sales and marketing.

Featured Image: Petuum