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“We write algorithms that take observational data from satellites, pixels, and turn them into real-time insights about our world. The applications for this technology extend from farming to government and, as you might expect, finance. We’ve got hedge funds knocking on our door almost every day, but our ambitions will take us far beyond the business of investing.”

“We use artificial intelligence, via evolutionary algorithms, to explain and predict complex ecosystems, such as patient outcomes in health care. Our platform does a good job of predicting financial markets. Still, we’re not interested in being a financial services company in the long run. We’re interested in explaining complexity and predicting successful outcomes in a range of domains.”

“We model trust among entities in a network by examining social connections, online interactions and backgrounds. Our ability to predict trust- and creditworthiness will have a huge impact on the peer-to-peer industry that’s developing, such as in ride sharing or apartment renting. And, of course, it’ll also create a massive opportunity in financial services, but that’s not our focus.”

Will the financial services industry soon be challenged by technology entrepreneurs with little initial — or no exclusive — interest in the investment business? The three abridged and anonymized quotes above, from real people running real technology companies, would seem to suggest that, yes, that’s increasingly probable. Which is not to say that companies aren’t emerging from Silicon Valley to target bloated segments of the financial services industry (that is, pretty much all of the financial services industry) — they definitely are. But we’ve noticed something rather more profound in the past year: The hot technologies being developed today will offer unparalleled insight into the complex world around us, and the applications to the entire domain of finance and investing are countless.

One example: The ascendance of nonbiological intelligence means computing systems will learn and process many types of inputs far faster than even the most-expert individuals. Once experts partner with the systems, these man-­machine teams will become extremely competent at rules-based goal seeking. The days of using scarce computing resources to model complex systems — backcasting, calibrating, validating and eventually forecasting — are nearly over. Massive and scalable parallel systems are now available for rent by the second, and the implications of having access to almost limitless computing environments to attack the largest data sets imaginable augur a paradigm shift in the discovery and communication of relevant data patterns. Machine-learning science and technologies are increasingly agnostic to the internal mechanisms of models. Rather, the scale of data that can be evaluated and processed in real time on massive grid networks will allow systems to inform their operators as to what the key variables are, instead of being restricted by top-down architectures.

In short, a growing number of computing systems and technologies will empower people, organizations, networks and information in transformative ways. Service industries will be particularly affected, as they often require human, labor-intensive analytics and networking to scale. But if technologies can help people network and analyze faster and better, some of the companies in the industries that provide these very services will face an existential challenge. As with the rise of computing and the Internet, we expect new technologies in the coming decade to challenge service industries, such as finance, in ways that few people today appreciate.

What does all this mean for institutional investors? Well, it means that computer systems may soon do for them directly what asset managers have been doing as third parties. It means the private club of hedge funds, which you once happily paid 2 and 20 to join, won’t be as exclusive as it used to be. It means the mythical “black box” that some asset managers use to drive outperformance — and reinforce their own market power — will be democratized. It means we will see a gradual emancipation of allocators from high-cost intermediaries and a dramatic move on the part of institutional investors toward much more flexible, and affordable, technology companies.

We think technology has reached a tipping point in finance. Up until now advances have served to empower private sector intermediaries, which traditionally have been relied upon to allocate capital to its highest use, at the expense of the big asset owners. Indeed, academic research shows quite clearly that smart financial intermediaries have used technology in the past few decades to obtain higher rents and reinforce their competitive advantage within uncompetitive markets. This undoubtedly results from the fact that technology often came with opacity and complexity, which was (and is) a recipe for high fees and compounding economies of scale flowing back to intermediaries. Indeed, to access the most-powerful technologies, institutional investors and others have had to work with the most-expensive gatekeepers.

At any rate, we believe Silicon Valley will help the community of institutional investors turn the tables. Ultimately, technology will help them streamline and strengthen operations, manage and distribute knowledge, access unique (and heretofore expensive) markets and level the playing field with the private financial services industry. As institutional investors adopt innovative technologies, we expect the very nature of financial intermediation to evolve. And that should be universally positive for our financial — and, indeed, our capitalist — system. For finance to function effectively, pensions, endowments, sovereign funds and other long-term institutional investors need to behave like highly sophisticated financial consumers. But to do that, they will need help from aligned intermediaries providing new and powerful technologies.

The authors of this article straddle the worlds of academia, technology and finance. Ashby is executive and research director at the Global Projects Center (GPC) at Stanford University and a senior adviser to the Office of the Chief Investment Officer of the Regents at the University of California. Daniel is CEO of Kensho Technologies, a Google Ventures– and Goldman Sachs Group–backed financial technology company that builds analytics platforms for the financial industry. Daniel also runs the financial technology research project at Stanford’s GPC. We are both passionate about the role technology will play in financial services, and we have dedicated much of our professional lives to this topic. We are both entrepreneurs, investors, advisers, researchers and writers, which means that listing all our potential conflicts would more or less consume the remainder of this article. For that reason, we’ve decided not to use the names of any commercial entities from which we could hope to derive even a possibility of economic benefit. This will allow us to use insights from our own experiences without (we hope) being accused of talking our own book.

Our purpose in this article is not to sound an alarm on Wall Street about the threat technology presents to its business — even if that’s a by-product of our writing it. Rather, we want institutional investors and Silicon Valley to better understand how they might work together in the years ahead. In short, this is not a story about the demise of global financial centers as much as it’s a story about the rise of a virtual financial center. And we believe this virtual financial center will be extremely powerful, providing institutional investors with an entirely new place to operate and achieve their objectives.

The job of an investor is to take money and turn it into more money. Put another way, the product that all institutional investors create is the same: They make returns. That’s all investors really do. And to achieve this, they all use, for the most part, the same inputs: To their initial stock of money they add a healthy amount of human capital, a dash of informational advantage and a dose of process. Persistent outperformance requires an investment organization to apply high-caliber people and efficient processes in creative ways to develop proprietary sources of information and, ultimately, knowledge. It’s this knowledge that allows investors to generate outperformance.

In thinking about the future of institutional investment, especially how technology will change the way investors do business, it’s useful to revert to these three inputs — people, process and information — and how they can come together to create knowledge. Whatever your unique approach to investing may be, its component parts likely fall into one of these categories. “People” refers to the talented individuals who drive returns for investment organizations. Talent is critical in the investment business, which is why skilled investors have sufficient leverage to get paid more than skilled professionals in any other industry. “Process” refers to the decision-­making inputs required by an organization to execute on its strategic plans and achieve its long-term objectives. Process also refers to governance, which is a critical factor for success, as the board has the ability to give the organization the resources it needs to achieve its objectives. “Information” refers to the insights and, ultimately, knowledge used by investors to make decisions. This may refer to the algorithms running in a black box, to a highly cultivated network of trusted individuals or to the theoretical models in a finance textbook. It’s important to note that not all information is of equal importance, and informational advantages are crucial. It’s for this reason that some investors are willing to push their information gathering to the boundaries of what is legal.

In our view, these three inputs and the way in which they are combined offer a framework for considering the key factors that drive success or failure among investors. These inputs are often of different qualities and combined in different ways to achieve similar return objectives. For example, the endowment model of institutional investment is based largely on informational advantages; the best endowments are adept at leveraging their networks to identify and access top managers to drive high performance. The Canadian model of institutional investment is based largely on process and human resources; institutions that follow it pay competitive salaries and manage assets internally, reducing fee overhang and improving their ability to think creatively about portfolio construction. The traditional model of institutional investment, which outsources all of the assets to external service providers, assumes that people and information are best procured outside the confines of an institutional investment organization.

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