Nick Beim

Thoughts on the Economics of Innovation

Clara Lending: A Big Swing

Screen Shot 2016-08-17 at 7.12.02 AM

There are few markets larger or more important to the US economy than the consumer mortgage market, which consists of $1.5 trillion in annual originations. Or more emotionally important to consumers, for whom homes represent an opportunity to build stability, a family and a better life.

Or more structurally broken. As was made clear in 2008, the mortgage market is fragmented into tens of thousands of companies in many different layers — brokers, originators, servicers, securitizers, government sponsored enterprises — whose complex interactions add costs, skew incentives and obscure risks, sometimes with devastating results.  

If one were seeking to reimagine this industry from scratch, the core problem to solve is much simpler than all this complexity suggests. On one side of the market you have consumers seeking low-cost financing for their homes. On the other side, you have the U.S. government, which finances more than 70% of consumer mortgages through Fannie Mae, Freddie Mac and the Federal Housing Administration and sets clear variables for the qualified mortgages it will subsidize.  

Why can’t one build an online platform to sit between these two sides of the marketplace, bringing transparency, lower costs, integrated data and a delightful consumer experience? That is is the vision of Clara Lending, a recent investment we’ve made that represents a big swing by its founders in one of the most important consumer markets there is. Clara is not simply reimagining the front end of the consumer mortgage experience. It is reimagining the entire mortgage bank from the ground up with software and data.  

The founders know this market unusually well and are as motivated as much by the social good the company can do as they are by the economic opportunity it represents. Jeff Foster, Clara’s cofounder and CEO, served as a senior policy advisor at the US Treasury during the first term of the Obama Administration to help fix the mortgage market and understand where the core data and incentive problems were. Lukasz Strozek, Clara’s cofounder and Head of Product and Technology, was previously a senior technologist at Bridgewater Associates, the world’s largest hedge fund, where he focused on translating complex processes and risk analyses into software.

If Clara is successful, it will lower mortgage financing costs for consumers and bring transparency and trust to an industry that tends to lack both. It will also bring transparency and integrated data to the mortgage supply chain, reducing macroeconomic risk and providing regulators with a clearer view of the market. It is a company we believe can create enormous value and bring enormous social benefit, the kind of investment we are most eager to make.

This post was originally published on Medium


The Barbell Effect of Machine Learning

shutterstock_225928441 (1)
.

If there is one technology that promises to change the world more than any other over the next several decades, it is arguably machine learning. By enabling computers to learn certain things more efficiently than humans and discover certain things that humans cannot, machine learning promises to bring increasing intelligence to software everywhere and enable computers to develop ever new capabilities – from driving cars to diagnosing disease – that were previously thought impossible.

While most of the core algorithms that drive machine learning have been around for decades, what has magnified its promise so dramatically in recent years is the extraordinary growth of the two fuels that power these algorithms – data and computing power. Both continue to grow at exponential rates, suggesting that machine learning is at the beginning of a very long and productive run.

As revolutionary as machine learning will be, its impact will be highly asymmetric. While most machine learning algorithms, libraries and tools are in the public domain and computing power is a widely available commodity, data ownership is highly concentrated.

This means that machine learning will likely have a profound barbell effect on the technology landscape. On one hand, it will democratize basic intelligence through the commoditization and diffusion of services such as image recognition and translation into software broadly. On the other, it will concentrate higher-order intelligence in the hands of a relatively small number of incumbents that control the lion’s share of their industry’s data.

For startups seeking to take advantage of the machine learning revolution, this barbell effect is a helpful lens to look for the biggest business opportunities. While there will be many new kinds of startups that machine learning will enable, the most promising will likely cluster around the incumbent end of the barbell.

Democratization of Basic Intelligence

One of machine learning’s most lasting areas of impact will be to democratize basic intelligence through the commoditization of an increasingly sophisticated set of semantic and analytic services, most of which will be offered for free, enabling step-function changes in software capabilities. These services today include image recognition, translation and natural language processing and will ultimately include more advanced forms of interpretation and reasoning.

Software will become smarter, more anticipatory and more personalized, and we will increasingly be able to access it through whatever interface we prefer – chat, voice, mobile application, web, or others yet to be developed. Beneficiaries will include technology developers and users of all kinds.

This burst of new intelligent services will give rise to a boom in new startups that use them to create new products and services that weren’t previously cost effective or possible. Image recognition, for example, will enable new kinds of visual shopping applications. Facial recognition will enable new kinds of authentication and security applications. Analytic applications will grow ever more sophisticated in their ability to identify meaningful patterns and predict outcomes.

Startups that end up competing directly with this new set of intelligent services will be in a difficult spot. Competition in machine learning can be close to perfect, wiping out any potential margin, and it is unlikely many startups will be able to acquire data sets to match Google or other consumer platforms for the services they offer. Some of these startups may be bought for the asset values of their teams and technologies (which at the moment are quite high), but most will have to change tack in order to survive.

This end of the barbell effect is being accelerated by open source efforts such as OpenAI as well as by the decision of large consumer platforms, led by Google with TensorFlow, to open source their artificial intelligence software and offer machine learning-driven services for free, as a means of both selling additional products and acquiring additional data.

Concentration of Higher-Order Intelligence

At the other end of the barbell, machine learning will have a deeply monopoly-inducing or monopoly-enhancing effect, enabling companies that have or have access to highly differentiated data sets to develop capabilities that are difficult or impossible for others to develop.

The primary beneficiaries at this end of the spectrum will be the same large consumer platforms offering free services such as Google, as well as other enterprises in concentrated industries that have highly differentiated data sets.

Large consumer platforms already use machine learning to take advantage of their immense proprietary data to power core competencies in ways that others cannot replicate – Google with search, Facebook with its newsfeed, Netflix with recommendations and Amazon with pricing.

Incumbents with large proprietary data sets in more traditional industries are beginning to follow suit. Financial services firms, for example, are beginning to use machine learning to take advantage of their data to deepen core competencies in areas such as fraud detection, and ultimately they will seek to do so in underwriting as well. Retail companies will seek to use machine learning in areas such as segmentation, pricing and recommendations and healthcare providers in diagnosis.

Most large enterprises, however, will not be able to develop these machine learning-driven competencies on their own. This opens an interesting third set of beneficiaries at the incumbent end of the barbell: startups that develop machine learning-driven services in partnership with large incumbents based on these incumbents’ data.

Where the Biggest Startup Opportunities Are

The most successful machine learning startups will likely result from creative partnerships and customer relationships at this end of the barbell. The magic ingredient for creating revolutionary new machine learning services is extraordinarily large and rich data sets. Proprietary algorithms can help, but they are secondary in importance to the data sets themselves. The magic ingredient for making these services highly defensible is privileged access to these data sets. If possession is nine tenths of the law, privileged access to dominant industry data sets is at least half the ballgame in developing the most valuable machine learning services.

The dramatic rise of Google provides a glimpse into what this kind of privileged access can enable. What allowed Google to rapidly take over the search market was not primarily its PageRank algorithm or clean interface, but these factors in combination with its early access to the data sets of AOL and Yahoo, which enabled it to train its algorithms on the best available data on the planet and become substantially better at determining search relevance than any other product. Google ultimately chose to use this capability to compete directly with its partners, a playbook that is unlikely to be possible today since most consumer platforms have learned from this example and put legal barriers in place to prevent it from happening to them.

There are, however, a number of successful playbooks to create more durable data partnerships with incumbents. In consumer industries dominated by large platform players, the winning playbook in recent years has been to partner with one or ideally multiple platforms to provide solutions for enterprise customers that the platforms were not planning (or, due to the cross-platform nature of the solutions, were not able) to provide on their own, as companies such as Sprinklr, Hootsuite and Dataminr have done. The benefits to platforms in these partnerships include new revenue streams, new learning about their data capabilities and broader enterprise dependency on their data sets.

In concentrated industries dominated not by platforms but by a cluster of more traditional enterprises, the most successful playbook has been to offer data-intensive software or advertising solutions that provide access to incumbents’ customer data, as Palantir, IBM Watson, Fair Isaac, AppNexus and Intent Media have done. If a company gets access to the data of a significant share of incumbents, it will be able to create products and services that will be difficult for others to replicate.

New playbooks are continuing to emerge, including creating strategic products for incumbents or using exclusive data leases in exchange for the right to use incumbents’ data to develop non-competitive offerings.

Of course the best playbook of all where possible is for startups to grow fast enough and generate sufficiently large data sets in new markets to become incumbents themselves and forego dependencies on others, as for example Tesla has done for the emerging field of autonomous driving. This tends to be the exception rather than the rule, however, which means most machine learning startups need to look to partnerships or large customers to achieve defensibility and scale.

Machine learning startups should be particularly creative when it comes to exploring partnership structures as well as financial arrangements to govern them – including discounts, revenue shares, performance-based warrants and strategic investments. In a world where large data sets are becoming increasingly valuable to outside parties, it is likely that such structures and arrangements will continue to evolve rapidly.

Perhaps most importantly, startups seeking to take advantage of the machine learning revolution should move quickly, because many top technology entrepreneurs have woken up to the scale of the business opportunities this revolution creates, and there is a significant first-mover advantage to get access to the most attractive data sets.

This post was originally published in TechCrunch


Dataminr and the Science of Real-Time Information Discovery

Today Dataminr announced a $130m round of financing from a group of leading financial institutions and prominent financial thought leaders including John Mack, Vikram Pandit, Tom Glocer and Noam Gottesman.  

A number of friends have asked me about the company and what I find most interesting about it. This seemed like a good opportunity to highlight a few thoughts. 

What I find most interesting about Dataminr is that in addition to building a business, it is pioneering a new science. The science is real-time information discovery, and it involves sifting through the ever-growing tidal wave of real-time public data to identify and determine the significance of breaking events by their nascent digital signatures, as they happen. Sometimes these events are well-wrapped, for example by someone witnessing an event and tweeting about it, with others providing corroboration. Sometimes they aren’t, with algorithms figuring out what is happening by seeing thousands of facets of something larger. The company has a deep strategic partnership with Twitter that makes this kind of discovery possible. 

This new science is, without a doubt, very cool. It enables one to discover news before it’s news and market-moving information before markets move. It provides a kind of X-ray vision into what is going on in the world in real-time with a filter for what is significant, and to whom. All on the basis of publicly available data.

In a period of five months, Dataminr has become the real-time wire service used almost universally by major news organizations, beating out the next best service by over an hour and discovering troves of unknown unknowns that would never have otherwise come to light. It has become adopted by the lion’s share of leading financial institutions to have access to the frontier of breaking information in real time.  

What’s also interesting is how Dataminr will change the world. In my view most industries that rely on real-time information — an ever-increasing number — will be influenced by it, and some will be transformed by it. The wave of change began in the fields of finance, news and public safety, and I think will move quickly to risk management, security and PR. And undoubtedly to other verticals in ways that are difficult to predict. I am particularly excited about what the company and its technology can do to help save lives in the fields of public safety and humanitarian assistance.  

Dataminr is in the early days of a long journey, but it is already impacting the world in significant ways, and it’s exciting to be a part of.


The Big Data Revolution in News

Yesterday Dataminr, a big data startup based in New York, announced something pretty extraordinary: that it would become the news discovery platform for CNN. This seems like one of those watershed moments in the history of the news industry that could change the industry’s dynamics fundamentally, like the advent of news agencies or the launch of CNN itself.

How can a technology startup become the news discovery platform for the world’s leading news organization? Because today, breaking events typically leave discoverable digital signatures before they become news, and Dataminr discovers these signatures as soon as they become algorithmically recognizable.

Most of these signatures are on Twitter, since Twitter has become the natural place that hundreds of millions of people post things they deem interesting, important, surprising, funny, scary, scandalous, or otherwise worth sharing – anything, in short, they deem newsworthy. No matter how effective any company’s news-gathering organization, it simply can’t beat the scale of this discovery system.

Most interestingly, Dataminr algorithmically discovers, qualifies, categorizes and communicates breaking events in real time. As they happen. This is an extremely difficult technological feat to pull off. There are half a billion to a billion tweets per day, and Dataminr’s algorithms process this stream of data and associated metadata in real-time to discover even the smallest micro-events as they happen and determine their significance, relevance and actionability.

How Well Does It Work?
How well does this work? In short, very well, both because there is so much signal on Twitter and because Dataminr has developed and honed its algorithms with an outstanding team of data scientists over the past three years.

One particularly memorable example of the kind of event discovery Dataminr excels at is the assassination of Osama bin Laden. Dataminr’s algorithms discovered the news on the basis of 19 tweets in a 5-minute period on May 1, 2011. The algorithms used signal pattern recognition, linguistic analysis, sentiment classification and cross-referencing with third-party data sources to identify the news. Dataminr alerted its clients of the news at 10:20pm. At 10:24pm, Keith Urbahn, the former Chief of Staff to Defense Secretary Donald Rumsfeld (not the country music singer), provided partial confirmation in his own tweet: “So I’m told by a reputable person they have killed Osama bin Laden. Hot damn.” The first move in S&P Futures caused by the news occurred at 10:39pm, and Bloomberg and the New York Times began reporting the news at 10:43pm. Quickly the news spiraled into one of the most viral events in Twitter’s history, with messages increasing from 19 in a 5-minute period to 20,000 per minute 30 minutes later.

Through its use of very sophisticated event discovery technology, Dataminr beat major news sources to the punch by 23 minutes on the biggest story of the year, and one of the biggest of the decade. Pretty cool stuff.

Read More