KZen is about to release ZenGo, a mobile app to manage your cryptocurrencies securely and more easily. There are already countless crypto wallets out there, but the startup thinks they’re all either too complicated or too insecure.

If you own cryptocurrencies, chances are they’re sitting on an exchange, such as Coinbase or Binance. If somebody manages to log in to your account, nothing is stopping them from sending those assets to other wallets and stealing everything.

Worse, if somebody hacks an exchange, they could potentially divert cryptocurrencies from that exchange’s wallets. In other words, leaving your cryptocurrencies on an exchange means you give your assets to that exchange and hope they properly take care of them.

On the other end of the spectrum, you can manage your private keys yourself and rely on a hardware wallet from Ledger and Trezor. The learning curve is too hard for many people. And if you don’t follow instructions properly, you might end up losing access to your wallet or accidentally sharing private keys.

Enough about other wallets, let’s talk about ZenGo. Former TechCrunch editor Ouriel Ohayon and his team think the perfect wallet app involves a smartphone you own paired with ZenGo’s servers.

The company uses threshold signatures, which means that you need both ZenGo’s servers and your smartphone to initiate a transaction. If you lose your device, you can recover your funds. But the startup can’t access your cryptocurrencies on its own.

Behind the scene, ZenGo still uses a public key and private secrets, but everything is completely transparent for the end user.

When you set up your wallet, two private secrets are generated separately and stored in multiple ways — one part is on your smartphone, the other is on the servers. You need both parts to sign a transaction. If you back up your device part to ZenGo’s servers, you can recover all parts in case you lose your device, for instance.

ZenGo can’t directly access the second part on its own because it is encrypted using a decryption code that is stored on your iCloud account. But accessing your iCloud is not enough — if you want to recover your wallet, you need to prove your identity.

That’s why the company stores a 3D biometric face map to let you restore your wallet on a new device. The company partners with ZoOm so that you can create a face map from any smartphone with a selfie camera.

The security model has been open-sourced and I hope many security experts will try to find vulnerabilities. That’s the only way you can know for sure that it’s a secure system.

All of this sounds complicated, but most users won’t even realize what’s happening. I tried the app and it’s a well-designed mobile app. Right now, it only supports Bitcoin and Ethereum, but more assets are on the way. The company tracks your public addresses to notify you when you receive funds.

The app isn’t available just yet. It should launch as a beta this week and arrive in the stores pretty soon.

Source: Tech Crunch

Portfolio co-founder: Our other investors want to participate but our lead wants to take most of the round.

Me: OK

Portfolio co-founder: So that means pro-rata is going to be tough.  

Me: Let’s see what everyone says.

A few days later.

Portfolio co-founder: The math worked out. Some people didn’t do their pro-rata and others did more.

Me: In theory, this shouldn’t happen because everyone is doing their pro-rata, but this is usually how things seem to work out. The round wasn’t going to be put at risk over pro-rata.

We’re always curious to see how rounds come together when there is limited capacity for both new investors and existing investor pro-rata. For the most part, there is supposed to be one core investor strategy; the maintainers, who use reserves and then opportunity funds or SPVs to avoid or minimize dilution. Sometimes there are also accumulators, who use multiple rounds to expand their ownership, but this is more common in private equity outside of venture capital.

The maintainers are pretty well understood. They have the typical $1 in reserve for each $1 invested, mirroring a common strategy espoused by some of the best VCs. USV shared a great example including fund allocation assumptions. Accumulators are a little more surprising to meet, but Greenspring, which is uniquely positioned to observe a lot of early-stage managers, hint that one of their top performing managers uses the accumulator strategy to get to more than 20 percent, fully diluted at exit. That’s not the whole story though, because, unlike USV, the strategy also involves some additional important assumptions, most notably investing in less-competitive geographies.

We’ve seen other allocation strategies, but we don’t see a lot written about them. For example, some investors tend to be among the first checks and, going through our co-investments with them, it’s clear they don’t always take pro-rata, but don’t seem to fuss about it. Here’s a great example of how one of today’s very best seed-stage investors, Founder Collective, thinks about this:

We dilute alongside our founders over time. So we have the same incentives as our founders to increase the value of the company in future financings.

It’s easy to dismiss this as founder-friendly at the expense of LPs, but I suspect Founder Collective’s LPs don’t see it that way at all. It’s hard to know how often this positioning leads to a higher win rate on competitive deals, but let’s assume there is little difference. Does the math work?

Let’s assume a VC is buying 20 percent of the company and then riding the dilution train down to a fully diluted 5.2 percent on exit at Series F (thanks to Fred Wilson again; in this example, we’re using one of his recent frameworks with these exact numbers). For a $50 million fund, this works just fine. Interestingly, it looks similar to the result for a $100 million fund with reserves, but the later assumes that they can always secure pro-rata and they can make use of opportunity funds to get a bit more upside.

We’ve discussed this a lot as we deployed our last fund. The vast majority of people insisted we needed $1 for every $1 invested, but we found that, thanks to our fund size, the math seemed to work without significant reserves if we purchased enough ownership upfront and, as Founder Collective notes, it seems to align better with founders and our growth-stage co-investors.

Longer funnel (not wider)

We’ve seen two major changes since we first started investing 12 years ago. The first is well-reflected by a recent deck shared by Mark Suster at Upfront, and highlighted in the slide shown below. It seems like the top of the funding funnel is getting wider.

It’s true that seed stage has grown 3x in the last decade. But that doesn’t necessarily mean the funnel only got wider. It also made it taller, like the image below.

One way to think about this — what used to be a sequence of “seed, A, B” is now, often, but not always a new sequence of “pre-seed, seed and seed+.”

Series A investments are totally different today than they were 10 years ago. But the Series A round is much more competitive because a lot of new money has shown up to play here and this makes accumulation and maintain models much harder, especially for seed and Series A stage-focused funds.

Who are these new players adding to the competition? Some are new VC funds, but a lot of them are corporate VC (CVC) funds.

Where is all this CVC money going? We’re pretty sure it’s not in pre-seed or seed, though there is some CVC fund of fund activity into seed funds, but that’s not reflected in this data. And we’ve only seen a few instances of seed+ CVC activity. Interestingly, to find a good example of this, you probably don’t have to look further than Lyft’s S-1, where GM and Rakuten join better-known tech CVC Alphabet.

Regarding the founder conversation referenced earlier, the round is coming together because of a strategic investor who is leading it. This has become more common. Like Lyft’s team, founders understand tech and value sector-specific corporate investors as partners.

We don’t think we’ll see a slowdown in CVC interest any time soon because, much like their big tech counterparts, incumbents in sectors from transportation and real estate to energy and infrastructure all realize that the startup ecosystem is now an extension of their product development process — VC and M&A are now an extension of R&D.

It’s not just that there is more money competing for Series A or B deals now. That money has different goals beyond pure financial returns and the value add is different from VCs. CVCs often bring distribution, ecosystem and domain expertise. So the end result is more competitive A or B rounds and more complex pro-rata discussions.

Strategic pro-rata shuffle

Founders are still trying to sell no more than 20 percent of their company, while traditional VCs are trying to buy 20 percent and we still have to figure out pro-rata for existing investors while making room for growing interest from strategic investors.

For Urban Us, we’ve embraced these new round dynamics — they may make growth-stage allocations a bit more tricky, but strategic investors can deliver a lot of value. One clear result — it’s sometimes better for us not to take our pro-rata at series A.

High conviction before Series A

We tend to think of high conviction as a Series A idea — i.e. Series A investors who accumulate, maintain or use opportunity funds. But the same concept is now at work in the tall part of the funnel — the two or three stages before Series A.

We’ve long been fans of accelerator models like YC, Launch or Techstars. We’ve co-invested with all of them. While there was a sense that “not following” presented signaling risk, accelerators have found creative ways to sidestep the issue — for example, joining rounds only if there is another lead. So this means they can concentrate holdings before Series A.

We now have our own accelerator, URBAN-X, because we’re best positioned to help address some unique challenges for the urbantech companies we’re looking to back. This allows us to be the first investor in most of our portfolio companies. And we can own enough of the company before Series A so we can still achieve our fully diluted ownership targets on behalf of our LPs.

As we look over scenarios related to when we first invest or when we think it will be hard to get pro-rata, we can find a few different paths to a target ownership position at exit. Some variations are shown below reflecting our approach for our newest fund.

The math

Obviously there are many different paths to ownership, especially in a world with two or three rounds happening before Series A. We’ve run a few simulations to understand the impact of different follow-on strategies. To explore different seed-stage allocation approaches, we modified Fred Wilson’s “Doubling Model” to explore a few of the variations. Only one change — we replaced Series A with seed+ as it’s more inline with what we’ve seen. It’s also important because it implies one less round of dilution in some seed strategies. We also assumed most seed investors invest in syndicates, so they don’t buy 20 percent unless they’re on the large end of fund sizes – i.e. $100 million+.

We explored what happens when seed investors make a single investment to buy 10 percent of a company and never follow-on and how might that compare to selective B and C-stage follow-ons or using progress from seed to seed rounds to avoid dilution on more promising companies. There is also the question of the implied fund size and number of investments — if you can make high conviction bets early, you get to make more investments even with a relatively small fund. But eventually you bump into time constraints for partners — getting to 40 deals with two partners can work, but presumes you are not a lone wolf partner and that you make hard choices about where to allocate time — which often seems harder than allocating money.

Up to about $50 million there are a range of possible strategies that can work, but diluting with founders allows more investments, even with smaller funds versus more traditional aggressive follow-on. More deals may be essential to the success of this model. Here’s our modified version of the doubling model (changes to the model are noted with blue cells).

Diluting alongside founders

VCs routinely remind founders that they shouldn’t worry about dilution because they will have a smaller share, but the pie will be bigger. Mostly this math works for founders, so why not VCs? Founder Collective is the only other firm we found that is explicit about aiming for this result. And this may be even more necessary today to make room for more strategic VCs to join traditional VCs.

At Urban Us our investment model is focused on getting fully diluted ownership before Series A. If we can do some pro-rata or sometimes if we need to do a bridge to buy teams more time, we’ll do that. And we’ll be equally excited when founders are able to bring in great new investors to help them through their next growth stage, regardless of their allocation strategy.

Source: Tech Crunch

The Bessemer Process patented in 1856 by Sir Henry Bessemer is one of the inventions most closely associated with catalyzing the second industrial revolution. By reducing the impurities of iron with an innovative oxidizing air blast, the process ushered in a new wave of inexpensive, high-volume steelmaking.

Bessemer decided to license his patent to a handful of steelmakers in an effort to quickly monetize his efforts. But contrary to expectations, technical challenges and monopolistic greed prevented large steelmakers from agreeing to favorable licensing terms.

In an effort to drive adoption, Bessemer opened his own steel making plant with the intention of undercutting competitors. The approach was so successful that each partner in the endeavor walked away from the 14 year partnership with an 81x return.

Some 162 years later, new businesses continue to struggle to convince customers to adopt new technologies — even when it’s in their best interest. Following in the footsteps of founders like Bessemer, today’s innovative startups are discovering that it often makes more sense to launch “full stack” businesses that provide a traditional service optimized with proprietary automation measures.

Chris Dixon of Andreessen Horowitz popularized the term “full stack startup” in 2014, just before the deep learning revolution. In his words, a full stack startup is a company that “builds a complete, end-to- end product or service that bypasses existing companies.”

The full stack methodology gave birth to companies like Uber and Tesla prior to the apex of the deep learning revolution. And in today’s AI-first world of data and human labelers, full stack startups are poised to play an even more important role in the startup ecosystem.

Going full stack comes with the advantage of being able to operate outside traditional incentive structures that limit the ability for large players in legacy industries to implement automation measures.

(Photo by Andrew Spear for The Washington Post via Getty Images.)

What does DIY AI look like?

Startups like Cognition IP, a BSV portfolio company, and Atrium are good examples of this. On paper, these businesses look very similar to traditional law firms in that they employ lawyers to practice patent law and startup law, respectively. But while traditional law firms often don’t automate due to the natural incentives associated with hourly billing, full stack startups are incentivized by consumer adoption so they have much to gain from developing a faster, cheaper, better strategy.

In addition to rejiggering old incentive structures à la Bessemer, going full stack opens up opportunities for companies to integrate labeling workflows into more traditional roles, to reap the full benefits of virtuous feedback loops, and to avoid countless complex process integrations.

Data labeling is a critical responsibility for startups that rely on machine learning. Services like Amazon Mechanical Turk and Figure Eight work well when startups have relatively manageable data labeling responsibilities. But when labeling and human-plus-machine cooperative decision-making are a core part of everyday operations, startups often have to hire employees to manage that workflow internally.

Scaling these teams is expensive and operationally intensive. Going full stack opens up opportunities for companies to integrate labeling workflows into other jobs. Employees traditionally tasked with performing a consumer or enterprise service can take on the extra task at reduced expense. And if their role is assisted by a machine, they will gradually become more productive over time as their assistive models get more accurate with more labeled data.

A second and inherently related benefit of going full stack is that these startups are able to generate – and own – powerful virtuous data feedback loops. Owning data flows creates more impressive moats than merely locking down static data sets. Deep Sentinel has a natural moat in the consumer security space, for example, as it not only has accurate classifiers, but accurate classifiers that continue to improve with real world data generated in an environment it can control.

Courtesy of Flickr/Tullio Saba

Leveraging automation is a matter of balancing risks and rewards

In 1951, Ford’s VP of Operations, Del Harder, decided it was time to upgrade the company’s lines with a more fully automated system for moving materials through the production sequence. It ultimately took five years of tinkering at Ford’s Cleveland Engine Plant before the technique was ready to scale to other factories. By chaining together previously independent parts of the production sequence, Harder had created new frustrating interdependencies.

Founders today going after traditional industries like manufacturing and agriculture similarly understand that the devil is in the details when it comes to scaling. The clear advantage to startups subscribing to the full stack methodology is that they only need to worry about integrating once with their own processes.

But on the flip side, going full stack does come with its own significant scaling expenses. Venture capital as a financing vehicle only makes sense to a certain point with respect to risk, margin and dilution, so many founders attempting to execute this strategic playbook have turned to debt financing.

Fortunately we have been in good economic times with low interest rates. Traditional full stack businesses like Tesla and Uber have both raised significant debt, and even up-and-coming players like Opendoor have turned to this financing strategy. A nasty economic downturn could certainly throw a wrench into things for just about everyone.

Progress in technology is cyclical and success is heavily dependent on execution within extremely narrow opportunistic bands of time. It’s debatable whether capital intensive, venture-backed companies like FedEx and Apple could have been successful if they were started in a different fundraising environment.

Like countless other automation technologies that preceded machine learning, the winners of the deep learning revolution will be startups whose technologies are optimized to work side-by-side with humans to generate outsized returns. Going full stack is difficult, expensive, and not the only way to win, but it’s an under-appreciated strategy that’s extremely relevant for today’s machine learning-enabled startups.

Source: Tech Crunch

“Welcome to the United States of America.”

That’s the first thing you read when you find out your green card application was approved. Those long-awaited words are printed on fancier-than-usual paper, an improvement on the usual copy machine printed paper that the government sends to periodically remind you that you, like millions of other people, are stuck in the same slow bureaucratic system.

First you cry — then you cry a lot. And then you celebrate. But then you have to wait another week or so for the actual credit card-sized card — yes, it’s green — to turn up in the mail before it really kicks in.

It took two years to get my green card, otherwise known as U.S. permanent residency. That’s a drop in the ocean to the millions who endure twice, or even three times as long. After six years as a Brit in New York, I could once again leave the country and arrive without worrying as much that a grumpy border officer might not let me back in because they don’t like journalists.

The reality is, U.S. authorities can reject me — and any other foreign national — from entering the U.S. for almost any reason. As we saw with President Trump’s ban on foreign nationals from seven Muslim-majority nations — since ruled unconstitutional — the highly vetted status of holding a green card doesn’t even help much. You have almost no rights and the questioning can be brutally invasive — as I, too, have experienced before, along with the stare-downs and silent psychological warfare they use to mentally shake you down.

I was curious what they knew about me. With my green card in one hand and empowered by my newfound sense of immigration security, I filed a Freedom of Information request with U.S. Citizenship and Immigration Services to obtain all of the files the government had collected on me in order to process my application.

Seven months later, disappointment.

USCIS sent me a disk with 561 pages of documents and a cover letter telling me most of the interesting bits were redacted, citing exemptions such as records relating to officers and government staff, investigatory material compiled for law enforcement purposes, and techniques used by the government to decide an applicant’s case.

But I did get almost a decade’s worth of photos taken by border officials entering the United States.

Seven years of photos taken at the U.S. border. (Source: Homeland Security/FOIA)

What’s interesting about these encounters is that you can see me getting exponentially fatter over the years while my sense of style declines at about the same rate.

Each photo comes with a record from a web-based system called the Customer Profile Management Service (CPMS), which stores all the photos of foreign nationals visiting or returning to the U.S. from a camera at port of entries.

Immigration officers and border officials use the Identity Verification Tool (IVT) to visually confirm my identity and review my records at the border and my interview, as well as checking for any “derogatory” information that might flag a problem in my case.

The government’s IDENT system, which immigration staff and border officials use to visually verify an applicant’s identity along with any potentially barring issues, like a criminal record. (Source: FOIA)

Everyone’s file will differ, and my green card case was somewhat simple and straightforward compared to others.

Some 90 percent of my file are things my lawyer submitted — my application, my passport and existing visa, my bank statements and tax returns, my medical exam, and my entire set of supporting evidence — such as my articles, citations, and letters of recommendation. The final 10 percent were actual responsive government documents, and some random files like photocopied folders.

And there was a lot of duplication.

From the choice files we are publishing, the green card process appears highly procedural and offered little to nothing in terms of decision making by immigration officers. Many of the government-generated documents were mostly box-ticking exercises, such as verifying the authenticity of documents along the chain of custody. A single typo can derail an entire case.

The government uses several Homeland Security systems to check my immigration records against USCIS’ Central Index System, and verifying my fingerprints against my existing records stored in its IDENT system to ensure it’s really me at the interview.

USCIS’ Central Index System, a repository of data held by the government as applications go through the immigration process. (Source: FOIA)

During my adjustment-of-status interview with an immigration officer, my “disposition” was recorded but redacted. (Spoiler alert: it was probably “sweaty and nervous.”)

A file filled out by an immigration officer at an adjustment of status interview, which green card candidates are subject to. (Source: FOIA)

Following the interview, the immigration officer checks to make sure that the interview procedures are properly carried out. Homeland Security also pulls in data from the FBI to check to see if my name is on a watchlist, but also to confirm my identity as the real person applying for the green card.

And, in the end, two years of work and waiting came down to was a single checked box following my interview. “Approved.”

The final adjudication of an applicant’s green card. (Source: FOIA)

It’s no secret that you can FOIA for your green card file. Some are forced to file to obtain their case files in order to appeal their denied applications.

Runa Sandvik, a senior director of information security at The New York Times, obtained her border photographs from Homeland Security some years ago. Nowadays, it’s just as easy to request your files. Fill out one form and email it to the USCIS.

For me, next stop is citizenship. Just five more years to go.

Read more:

• Trump administration sued over warrantless smartphone searches at US borders
• Border agents can’t lawfully search data stored in the cloud
• The startup community must defend merit-based immigration
• A simple data analysis disproves the argument for building a border wall
• The startup community must defend merit-based immigration
• The hottest new space to disrupt is immigration
• Why Silicon Valley needs more visas

Source: Tech Crunch