MobileCoin, a cryptocurrency from the creator of Signal, just raised $30M for private mobile payments

A new privacy-centric cryptocurrency project with some big names on board just raised a round worth noting. On Tuesday, the team at MobileCoin announced that Binance Labs, the major blockchain incubator associated with the Binance exchange, led a $30 million round denominated in bitcoin and ether for the new cryptocurrency. MobileCoin will enjoy “priority consideration” for being listed on Binance as part of the relationship.

New cryptocurrency projects are a dime (or less) a dozen, but the legitimacy of an established name can make all the difference. Moxie Marlinspike, the founder of end-to-end encryption messaging app Signal and Open Whisper Systems, is one such name. As Wired reported in December, Marlinspike began working with MobileCoin as a technical advisor in August of 2017.

Marlinspike is joined by Joshua Goldbard, a general partner at hedge fund Crypto Lotus and MobileCoin technologist, and Shane Glynn, legal counsel, to help the company navigate the choppy waters of cryptocurrency regulation. Glynn has served since 2010 as senior product counsel at Google, though it’s not clear if he is leaving his longtime role for the new project.

In the MobileCoin whitepaper, published in December, the project’s creators describe its mission:

…Most attempts at building a compelling crypto-currency user experience unfortunately resort to trusting a third party service to manage keys and validate transactions. This largely sacrifices the primary benefits offered by crypto-currency to begin with.

MobileCoin is an effort to develop a fast, private, and easy-to-use cryptocurrency that can be deployed in resource constrained environments to users who aren’t equipped to reliably maintain secret keys over a long period of time, all without giving up control of funds to a payment processing service.

MobileCoin is being built on the Stellar blockchain, a popular ethereum alternative that some projects are flocking to for its scalability and speed, and will emphasize user privacy and integration into mobile messaging apps, including WhatsApp and Signal — two apps that use Marlinspike’s end-to-end encrypted Signal Protocol.

“MobileCoin is designed so that a mobile messaging application like WhatsApp, Facebook Messenger, or Signal could integrate with a MobileCoin wallet,” the team described in its whitepaper.

Marlinspike is a rare sort of reverse tech celebrity, a figure who eschews both spotlight and Silicon Valley-style excess and has instead cultivated quiet respect in digital privacy and cryptography circles. That makes him an odd fit for the fraud-laden universe of empty multi-million-dollar ICOs with no product to speak of, but it also means that MobileCoin is probably worth paying attention to. At the very least, the prominent cryptographer’s new project should amuse anyone who’s complained about the digital currency world’s habit of using the term “crypto” as shorthand for “cryptocurrency.”

MobileCoin has funding and talent, but it’s still very early days for the nascent cryptocurrency. As an incubator, Binance Labs concentrates on pre-ICO projects and MobileCoin will use the funding to “build out [its] team and processes” as it develops its product.

“A mobile-first, user-friendly cryptocurrency, like MobileCoin, plays a critical role in driving mainstream cryptocurrency adoption,” Binance Labs said of the funding. “The MobileCoin team and Binance Labs share a common vision and we are proud to be a supporter of what they are doing.”

Along with the news, MobileCoin announced that it is recruiting a “core team” of engineers:

“Specifically, we are looking for those who have worked on large systems (greater than 10,000,000 daily active users) in a senior role who enjoy working on low-level code. Direct memory access is a critical part of our problem set.”

Given the legitimacy of Marlinspike’s best-known project and his reticence to attach his name to things, it’s not unreasonable to give MobileCoin the benefit of the doubt, even if aspects of its raison d’être remain unarticulated. Beyond the core question of why a new cryptocurrency needs to exist at all, MobileCoin will need to position itself as a compelling alternative to existing mainstream mobile payment services like Venmo and PayPal for normal users.

MobileCoin will also face the full slate of regulatory challenges, including fraud prevention, that plague other digital currency projects, though given its stealthy behavior and the fact that one-third of the three-member team listed on its website represents legal counsel, its founders are don’t appear to be charging in recklessly.

“This is a journey and we are excited to build a simple system for trusted payments,” Goldbard wrote in the announcement.

In the digital currency realm, too much style — think celeb-endorsed ICOs and endless press release hype cycles — can signal a lack of substance. The reverse can be true too, and in MobileCoin’s case, a modest mission could be a strong signal for a compelling product a bit further down the blockchain.

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Four MIT students have launched DeepBench to democratize access to expert networks

New European financial regulations requiring fund managers at investment firms to pay banks for research and trading services separately could open the door for new entrants in the professional advisory services marketplace.

The rules, which were approved in 2014, but only took effect in January, are proving to be a boon for four MIT students who launched a company last year to try to grab some of the market.

DeepBench, founded by Devin Basinger, Yishi Zuo, Derek Hans and Nikhil Punwaney, is proposing some novel business model solutions to address what the MIT students see as flaws in the existing market — particularly around the use of expert networks in financial advisory services.

DeepBench co-founders Devin Basinger, Nikhil Punwaney, Derek Hans and Yishi Zuo

Expert networks are communities of experienced professionals in a given field. Fortune 500 companies, hedge funds, private equity firms and other entities rely on individuals from these groups for their insights and expertise. The biggest company in the expert network industry, Gerson Lerman Group (GLG), has nearly 50 percent market share and was on track to reach $400 million in revenue in 2016.

But GLG has had its share of troubles. The company played an integral role in providing the expert that passed confidential information to an SAC Capital trader, which was used as evidence in an insider trading case against the firm and its owner, Steven A. Cohen. The hedge fund ended up paying a record $1.8 billion in fines to the SEC (they did not admit wrongdoing in the case).

There is a significant opportunity to disrupt the expert networking space. As more experienced workers retire, some may want to continue putting their skills to use, albeit in a reduced capacity. Being a part of an expert network allows them to be available for clients who request their expertise in a flexible, convenient capacity. Facilitating this specialized knowledge sharing is a billion-dollar market for the taking.

Aside from established players like GLG and its European competitors, AlphaSights and Third Bridge, other startups like Clarity, Slingshot Insights, Catalant (formerly known as HourlyNerd) and Dūcō are also looking to transform the way expert networking is done. GLG is known to charge a group of four within a firm $100,000 for basic access to their network for a year. In comparison, these startups have different approaches and business models to improving the way clients access the expertise they need. Their efforts reflect two main segments within the expert network market: expert calls and project-based work.

DeepBench and Slingshot Industries are focusing their efforts on expert calls. DeepBench launched its current service in March 2017, which uses its “technology-driven, human-assisted” platform to connect individual clients with available experts for a 30 to 60-minute conversation at an agreed-upon rate. In addition, the startup does not require “learners” to sign long-term contracts or prepay, unlike other firms, allowing for greater client flexibility. Slingshot Industries matches groups of clients with similar interests to an expert to answer their questions. The group would crowdfund the cost for chatting with the expert.

Catalant and Dūcō have aimed for matching clients that need long-term projects completed with the relevant experienced contractor. These clients are looking for experts who are interested in extended-duration work. Catalant leverages its algorithms to quickly match prospective clients with the experts they are looking for based on the former’s search criteria.

Their goal is to make this process seamless, so more experts and clients will feel enabled to collaborate outside of a conventional consulting framework or contracting arrangement. Dūcō appears to take a more conventional approach to connecting clients and experts. The D.C.-based startup vets its pool of experts before offering them up to potential clients. Like Catalant, Dūcō uses matching algorithms to match clients with project work needs to experts ready to assist them.

As investors seek information to keep their competitive edge, and firms need outside help in solving internal problems, on-demand access to expert networks will become necessary. DeepBench currently has more than 1,000 registered experts for their closed beta platform. Currently, more than 20 clients are using the service. Most are top consulting companies, investors and product designers.

“We are focused on finding quality high-fit advisors right now instead of increasing the volume we can have available for clients,” Basinger said.

With a shift in E.U. financial regulations, expert networks are using their momentum in the Asian and U.S. markets to establish themselves in Europe. This specialized knowledge sharing can be shaped by startups like DeepBench as competition between firms continues to intensify.

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IoT ‘conversation’ and ambient contextuality

A few years back, I wrote about the way we communicate with our technology. It was obvious even then that a big game-changer would be enabling a reliable conversational interaction with technology in order to overcome the friction humans experience when we use our modern tools, be they apps, phones, cars or semi-autonomous coffee makers. Too much typing and swiping and app management crowds our experiences with our connected “things.”

To some degree, this game-changer has come to pass.

Voice interaction is now a big part of technology interface in everything from smartphones to virtual assistant/smart speaker products to connected home and vehicle solutions — and so it will be going forward. While this is marked progress, it is not really “conversation.”

For the most part, the state of voice interaction is more akin to commanding a four-year-old to do your bidding than having a useful, rich conversation with a friend or assistant. As we continue to minimize friction and advance usability of technology via voice, it is clear that more is needed. I’ll predict right here that the next big game-changer in technology interface is ambient contextuality.

Ambient contextuality hinges on the idea that there is information hidden all around us that helps clarify our intent in any given conversation. Answering the simple questions of who, what, where and when is now easier than ever as IoT continues to mine and mind the data of our lives. I once sketched out a derivative needs pyramid for IoT devices using the example of Maslow’s hierarchy of needs pyramid to chart a course for “thing-actualization,” whereby our technology could use analytics, learned logic and predictive behavior to establish groups and networks of things and enable other more “complex” things. The voice interfaces and natural-language processing technology on display in interactive speakers such as Amazon’s Alexa or Apple’s Homepod are examples of this actualization in action — predictive analytics and machine learning imbued into objects and interfaces to technology that collect data and collectively power progressively complex functions, often in real time.

But it is still not conversation. There is a new, nascent communications triangle between people, processes and things that fuels usability, and it still has a bit of its own growing up to do.

Deeper questions like how and why are also key to conversation for humans. To achieve truly conversational interactions, one or many of the answers to these questions not only need to be captured, but also learned and retained. Recently, Google has made some good strides into this for targeted types of online search. But we have to do much more before something akin to natural conversation emerges.

Establishing ambient contextuality to enable the kinds of conversations we do want to have is the actual end goal of all this connected stuff.

Most human conversation is abridged. Known quantities may not even be discussed, but they are deeply factored into interaction. A simple example is shifting from nouns and proper names to pronouns. “I asked about Dave’s vacation and Jen said she’d take him to the airport to kick it off right.” This may seem like a small thing, but think about how unnatural a conversation is when you cannot use human “shorthand.” Referring to every subject in every sentence by its proper name quickly becomes as uncomfortable as it is unnatural.

A simple definition of a conversation is an informal exchange of sentiment and ideas, and it’s the way people naturally communicate with each other. Informal conversation is contextual, cohesive and comprehensive. It involves a lot of storytelling. It ebbs and flows, jumps around in time and tense, references shared experience or knowledge to exchange new experiences and knowledge. It is inference infused and doesn’t require adherence to strict conventions. But this is pretty much the exact opposite of the way “things” are designed to communicate. Machine communication is specific to whatever technology drives it and is based on code. It is binary, resource-constrained, inflexible, standalone, purely informational and lacks context. It is rigid and formal. It is very much not storytelling.

This elemental difference in communication creates a usability gap, which we have traditionally bridged by forcing people to learn to “speak” machine — download a new app to control every new device, use this set of wake words or language constructs for one device and an entirely different set for another, update, update, update, and if-this-then-that for everything. It’s why so many “things” end up thrown in a drawer after two weeks, never to be used again. This is not the kind of conversation humans want to have.

Putting aside the creepiness factor and important privacy issues surrounding devices that constantly collect information about us, establishing ambient contextuality to enable the kinds of conversations we do want to have is the actual end goal of all this connected stuff. The aim is to smooth our experiences with our technology throughout the day and blur the seams enough to feel natural to us.

The challenge now is to make our machines “speak” human — to imbue them with context and inference and informality so that conversation flows naturally. DARPA has been working on it. So, too, Amazon and Google. In fact, most technology efforts are concerned with reducing interface friction. Improving the quality of our conversation is key to achieving that goal.

Development on IoT, augmented and mixed reality, Assistive Intelligence (my term for AI, but that’s an entirely different conversation) and even the miniaturization and extension properties on display in mobility and power advancements are all examples of the quest for that quality. Responsibly developed ambient contextuality, and ultimately natural conversation, will be better enabled by these technologies, and our lives will become much more conversational soon. Once we experience reliable and useful conversations with our technological world, I think we will all be hooked.

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Digit’s first move beyond saving money is a feature to pay down credit card debt

Digit, the developer of a wildly popular automatic savings mobile app, is moving beyond its core business with a new feature enabling users to pay down credit card debt from their Digit account.

Announced earlier today the new Digit Pay service, which uses savings in a Digit account to pay off credit card debt for any registered account.

The new feature works by enabling users to create a “credit card debt” goal in their Digit settings and activate the Digit Pay service. Digit automatically will begin to save money from a linked checking account — and use those funds to pay off credit cards. Credit card payments can even be prioritized through Digit’s boost feature.

So far, the Digit app has been used to save roughly $1 billion for its customers according to chief executive officer Ethan Bloch .

Bloch says that Digit has been focused on solving the biggest financial pain points for the most customers it can reach in the U.S. For the company, that meant starting with savings…. and moving on to the next biggest threat to customers’ financial health in the U.S. — debt.

Roughly 75% of the company’s customers have credit card debt (hi, my name is Jon and I’m a Digit customer).

In the U.S. there’s about $1 trillion of credit card debt outstanding — a stat that’s very no bueno for the U.S. economy. Add to that, an average U.S. household owes about $16,883 and pays about $1,292 in interest each year (credit card companies thank you).

For folks who need a refresher in how Digit works, the company’s app provides a service that connects to checking accounts from almost any bank . Digit’s software analyzes income and spending and then sets aside small amounts of money at intervals that won’t impact an account. The company offers a 1% annualized savings bonus for people who save with Digit for three months, and the service costs $2.99 per month after a free 100 day trial period.

Those savings are placed in a rainy day fund or toward any other financial goals that a user sets in the app. They can be customized, and the latest customization is this Digit Pay option.

It’s the first time that Digit is linking back out to other vendors and it paves the way for other services using the Digit balance.

One thing that users shouldn’t expect to see anytime soon is an investment feature in Digit, according to Bloch. “Digit was founded to make financial health effortless,” Bloch said. While investment tools are good for helping their users make more money, Bloch said they weren’t core to his view of financial health.

“We’ll be focused on those two… savings and credit card debt,” he said.

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