Will Janensch is Founder and CEO of TruSet, an Ethereum-based platform and ConsenSys formation which enables participants to collect, validate, and share business-critical reference data. By creating a foundational layer of trusted and machine-readable reference data, TruSet is establishing new, primary sources of truth for industry ecosystems and creating opportunities for innovation and efficiency in global markets. In December 2018, TruSet launched its first beta platform for crowdsourcing reference data on cryptotokens. In June 2019, TruSet launched its pilot platform for crowdsourcing and crowd-validating real estate market data alongside Imbrex, an Ethereum-based real estate transaction startup.

Previously, Mr. Janensch served as Co-Chair of the Wall Street Blockchain Alliance’s Research and Innovation Committee. WSBA is a non-profit trade association focused on digital currencies and blockchain technology for financial market professionals. Mr. Janensch’s previous roles also include Vice President of Competitive Intelligence at First Data Corporation, where he led financial and strategic analysis of public and private competitors in the payments space, a variety of senior roles at Thomson Reuters, and Manager of Strategy & Financial Analysis at SmartServ Online. Mr. Janensch earned an MBA from the Stanford Graduate School of Business and a BA from Yale University.

The Politic: What’s your background and how did you get involved in blockchain?

Will Janensch: I’m a graduate of the class of 1995, and I’m about to have my 25th reunion next year. I was a Theater and Literature Theory double major– not Computer Science. I ended up moving to New York, where in addition to doing theater, I also ended up doing a more IT-focused job at an investment bank. I decided that I didn’t want to do either of those as a career. 

I went to Stanford for an MBA, and when I left Stanford, I didn’t want to do the very typical startup route of Stanford graduates. When I graduated in 2001, the big tech/startup bubble popped and there weren’t any startup jobs available. I ended up at Reuters, a big financial info company which later became Thomson Reuters. I worked in two roles over the span of 11 years, ultimately serving in a senior strategy position. I dug into different parts of financial information for capital markets, electronic trading platforms, and digital financial services infrastructure. 

I ended up leaving Thomson Reuters and spent a short amount of time at a big credit card processing and data company called First Data. It was there, in January of 2015, that I was asked to do a short strategy write-up on Bitcoin and what I knew about Bitcoin at the time.

What knowledge did you have of Bitcoin at the time? What info did First Data want you to gather?

I knew the basic headlines. I knew that it was a digital currency with some unsavory sort of reputation. I’d heard about the Silk Road, which was a sort of Dark Web, e-commerce site which used Bitcoin as a means of payment for some illegal products. So, I didn’t have much more than a bad impression of Bitcoin. I knew there was this gold-standard vibe among that community, but that wasn’t my thing. 

First Data was looking for info as basic as whether e-commerce gateways should allow Bitcoin payments. As I read about the topic, I discovered that people were beginning to position “blockchain,” a technology which allows Bitcoin to exist but is distinct from Bitcoin or other cryptocurrencies, as the real innovation in Satoshi Nakamoto’s 2008 whitepaper. 

I had an epiphany when I learned about that distinction. All the light bulbs went off. Blockchain could transform the payment infrastructure at First Data and the credit card industries. It could reduce settlement times and the amount of assets required to be held against uncivilized trades. Lots of ideas came to mind as to how this tech could be potent, particularly in financial services. 

I ended up leaving First Data a few months later and took some time off. After having spent 11 years at Thomson Reuters and a year at First Data, I was pretty certain that I didn’t want to work for a big company. I felt like a cog, and I wasn’t really clear on the value of my work. I had felt that Thomson and First Data, as large incumbents, were on the losing side of new technological waves that were disrupting major market forces. And I wanted to be on the winning side.

What were your thoughts on the winning side of tech disruption at that point?

When that became clear to myself, I remembered my work on blockchain a few months earlier, and I was like, “Oh yeah, that will be the play.” I said to myself, “Take a year, don’t worry about money, and see what you learn about the space. See where you can play.” Honestly, I thought I would end up at a bank or a large consulting firm as the head of blockchain strategy, because that was my play– I was digging into the space, going to a lot of meetups, talking to people, and so on. Especially back then, but even to this day, you could go to three meetups a week on Bitcoin and blockchain. Ether also started coming out at that point. And so there were plenty of learning opportunities. 

I would go to these highly technical meetups, take notes, and do extensive research afterwards. I didn’t understand 90 percent of what people were saying, but I wanted to grasp complex topics like “elliptic-curve cryptography” nevertheless. I did that for a year, I consulted for a little, and I published a little piece on the Bitcoin block size conundrum. I was talking to banks and consulting firms, and when I was thinking about those issues and how they could be applied to financial services, one thing became clear.

There were these very cool ideas around the creation of blockchain-based digital assets, where the behavior of those instruments would be governed by smart contracts on-chain, and execution could be automated, oftentimes by external factors. I was thinking about those themes and their application to financial services. I wondered, “How could we think about these problems of inaccurate data, particularly around financial securities that exist in the market today?”

Thomson Reuters was one of the big players. They would feed that data to the network, and every customer of that data repeated the same cleansing processes to correct errors in the data feed. I said, “This sounds like a blockchain problem.” Here we have a distributed community of non-trusting actors who each need to reach agreement on a single version of a data set’s truth, and there must be an incentive system that both encourages that work and comes to a correct and trustworthy answer.

I thought, “that’s essentially what happens with blockchain,” where the data set on the Bitcoin is the accounts ledger, and the Bitcoin I’m talking about is the machine-readable data sets around financial assets. I was like, “Wow– I think there’s a real business here.” Everyone kept telling me that my idea was good, and I eventually started a company around that idea.

That’s actually the company I’m running today. The strategy has morphed since its inception, but it’s essentially a crowdsource and crowd-validation platform for business ecosystems to produce the reference data–relating to financial securities, crypto tokens, or financial assets–necessary to interact with one another. We’ve launched a couple of versions of that so far.

Wow– so you went from Theater and Literature Theory to blockchain technology?

One of the cool things about blockchain is that it’s of interdisciplinary interest. People from a computer science background can appreciate the infrastructure. Building the infrastructure for the Ethereum blockchain is basically creating a world computer where these nodes all collaborate on one massive computing structure.

But there’s also an economics and game theory perspective to be found in Satoshi’s whitepaper. My lens was certainly not the coding lens– it was the business strategy, economics, and game theory side. I thought, “Oh my gosh, this is such an elegant solution to the game-theoretical problem of third party trust.” Blockchain creates incentives so the only logical behavior is to do that which the network wants: to create the most accurate data set. 

The elegance blew my mind– and you can come at it from the other side too. Most of the people I work with are on the computer programming side, but I think it’s a cool technology regardless of your background.

What’s the background on TruSet, and what’s its relationship to ConsenSys?

My project/company is called TruSet, but we are incubated and effectively part of ConsenSys, a leading Ethereum blockchain technology company and incubator. The founder of ConsenSys is Joe Lubin. He’s one of the founders of the Ethereum network along with Vitalik Buterin, who wrote the whitepaper describing the Ethereum Network. 

The whitepaper pointed out that although blockchain is really powerful, the Bitcoin Blockchain is limited to payments. You can try to use some of the transaction package for alternative uses (e.g., to create other coins), but it’s ultimately pretty limited. He envisioned using the same concept, but instead of sending tokens of value, you would send packets of code. In that way, it’s a much more flexible and extensible platform for building lots of applications, including business solutions.

So, we became part of ConsenSys. Our original concept came from the data problem in traditional capital markets around financial securities. That’s a tough market because it goes head-to-head against Bloomberg and Thomson Reuters. We had a bit of a pivot movement–the ICO boom–in late 2017 with the explosion of token issuances launching on the Ethereum blockchain. That seemed like a really exciting innovation in general, and it seemed like a lot of things could get tokenized, because part of the attraction of the blockchain was the availability and trustworthiness of on-chain data.

You think, “Oh wow, this is solving a lot of those problems”– yet ironically, a lot of those traditional problems in capital markets were being recreated, or even worse, in the ICO world. It wasn’t the on-chain or smart contracts data– that was pristine. It was all the data about what these projects were, and aspects which weren’t contained in the ERC20 smart contract themselves. There wasn’t even a requirement to have a legal prospectus that’s pushed to a regulator because these weren’t securities.

There were whitepapers, blog posts, or commentary. There were websites that described these tokens, what they were going to be, what the project was behind them, and who the legal entities were– if there were legal entities. It was very hard to find trustworthy information for financial analysis. And It was impossible to get machine-readable information about the projects that you could plug into different models.

What applications/partnerships are you involved in?

The concept was that as more institutional players entered the market, there would be hedge funds or other firms using tokens. They were going to need machine-readable information to do pre-trade analytics, to do order routing, and to manage basic elements of portfolios such as risk management. 

We set out to apply our platform to that issue. We said that we could create a reference data set for tokens for use by institutional investors and portfolio managers. That’s the first application we launched. That project is up-and-running, and we have a few different companies using that data. Parts of ConsenSys are using that data, but it’s still pretty much in beta, and we’re exploring with different models to incentivize data creation. We have about 150 people on-boarded to our platform that are collectively producing this information in a way that forms a trusted data set.

We also recognize that the platform we built is data agnostic. We originally built it for bond data and then extended it to token data. But we started receiving requests from other industries, so we’ve launched a pilot real estate data platform in partnership with Imbrex, an Ethereum-based real estate transaction startup. We’re starting with really basic regulatory and sales agreement forms, but part of the idea is that there’s a large variety of real estate data that could use crowdsourcing and crowd-validation, plugging in property-listed data, which was sort of our first thought. 

We’re in discussions with a petroleum data company about oil well and extraction data that are also quite tough in terms of there being different sources of data which don’t agree and are necessary across the industry for a single source of truth. We’re open to applying our technology to other industries as well. 

A lot of companies (e.g., hedge funds) use proprietary data sets and data analysis to differentiate themselves and gain an edge. Do you ever run into situations where companies don’t want to share a consensus data set because of that?

It depends on the data set. Think about our starting example: the basic facts, terms, and conditions of bonds. For the most part, that’s not a data set that companies gain strategic differentiation from. The companies use that data.

If you are Goldman Sachs or a hedge fund, you need this data to plug into your pre-trade analytics or to perform risk analysis of your portfolio for accounting purposes. The proprietary analytics they use on top of that data are definitely a strategic differentiator, but the baseline data they plug in to those proprietary models shouldn’t be different. It’s a long-standing capital markets industry pain point that the industry needs reference data on securities (and now tokens) that’s machine-readable, objective, and accurate. 

For securities, that information is originally generated when a bank issues a security and publishes a prospectus to the SEC. The SEC makes those prospectuses freely available so investors can read a trusted legal document describing the security. But, those prospectuses are not machine-readable, so they can’t be consumed by the many different financial services software systems that need the data for mission critical analysis and processing.

The value add of vendors like Bloomberg or Thomson Reuters is that they take those PDFs and have a couple thousand people convert them in their data model. These vendors then sell that data set–of tens of thousands of securities–to the financial services industry as a data feed. But it’s complicated because there are lots of opportunities for mistakes in the process of converting information from prospectuses to machine-readable data. In the end, some items are inevitably wrong.

The customers of that data need it, so it’s worth the cost of buying it from Thomson Reuters or Bloomberg in machine-readable form– but they know there’s data they’ll need to fix, which is expensive and time consuming for the entire industry. That same data cleansing process gets repeated across every customer of this data in silos, creating an inefficient data ecosystem and a significant set of undifferentiated costs in the back offices of financial institutions world-wide.

But couldn’t there be one source that they all agree is valid? Then, financial institutions could each add their proprietary analytics and models on top of a common, trusted data set. In general, there isn’t that problem of conflicting incentives, where companies don’t want to share a single source of truth for this data set. There are some exceptions where a company may want to protect a proprietary data-cleansing process, rather than participating in a crowdsourcing and crowd-validation platform, but for the most part they are willing to share a single source of truth on these data sets.

Final thoughts?

A couple of things. If people are interested and they’re afraid of whitepapers, I urge them to read the Satoshi Nakamoto Bitcoin whitepaper from 2008. It’s not technical, it’s eight-pages long, and if you have any background in economics or computer science, you’ll get it immediately. At least for me, that paper is what really opened my eyes and allowed me to understand the technology and how it works in a way that reading other material just hadn’t given me. I highly recommend that people read the whitepaper even if they’re not technical. I found it super helpful and insightful.

The other thing is that there’s a lot of activity occurring in the space. I know the bloom is off the rose with the crash, but I think the tech is real, I think it’s going to be massively important and disruptive beyond financial services, and I think it’s really powerful in a lot of different use cases. I don’t think it’s too late to get into this space or too late to come up with cool new uses of this technology. So, I encourage people to get into it and to think about ways in which they can use this technology to solve real problems.

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