This chapter leads a whirlwind, curated tour of investment technology's six-millennium history—from clay tablets on up to digital ones. It teases out three dimensions of progress that have characterized advances in invest-tech across time: data latency, inferential depth, and resource efficiency. The chapter discusses patterns in how these dimensions are interrelated, and why they are important for institutional investors that are seeking to reorient their organizations around technology. This chapter also looks at how the tyrannous reign of spreadsheets is a poster child for what's presently amiss with investment technology.

This chapter conducts a deep-dive appraisal of the state of technology in institutional investing and dissects how it is holding institutional investors back from reaching their goals. It discusses ten "entangling" problems that our research indicates are to blame—including focusing on the wrong scales for tech projects, deprioritizing innovation, having isolated perspectives about technology, failing to cooperate with peers on tech, and more. The chapter also explains why some "scapegoats" that get blamed for institutional investors' tech troubles are mostly just hollow excuses (these include outsourcing, costs, and being "close followers").

This chapter provides an overview of current trends in cutting-edge technology that are relevant for institutional investors who aim to reorient around technology. It pays special attention to two such trends: openness and simplicity. The chapter covers four key classes of tools that can serve as the foundations of institutional investors' tech superpowers: artificial intelligence, alternative data, collaboration tools, and productivity utilities. The chapter discusses the balance that must be struck between understanding and efficiently deploying these tools. An appendix to the chapter gives flyovers of deep-learning and blockchain fundamentals.

This chapter supplies rubrics to help institutional investors analyze the suitability of specific technologies for their organizations. It begins by introducing a tool set to compare a candidate technology's impacts on the organization's opportunity set and resource budget. The chapter then provides several tools for analyzing how a technology may affect, and create value from, an organization's data, information, and knowledge resources. The chapter concludes with commentary about the role of cost-benefit analysis in assessing technology's fitness within the organization.

This chapter presents thirteen features that empirical research indicates will be needed for any institutional investor to successfully technologize. Some of these features are attainable in the very near term, whereas others may take time to cultivate. These features are divisible into two groups: "Core Attributes" needed to sustain tech skills in the long run, and "Sources of Advantage" that will more immediately help institutional investors brandish tech superpowers. This chapter gives an extensive discussion of features in each of these groups, and describes how they address traditional technological shortcomings.

This chapter launches a foray into the data-related tech superpowers that institutional investors can build. It distils empirical findings on the causes of institutional investors' current struggles with their existing data systems. The chapter discusses several ways in which institutional investors could beneficially rethink how they use data, by merging data-governance and data-management protocols; casting data as a collective process; focusing on retaining and augmenting more context around data sets; prioritizing data on unlisted, private assets; and nixing the binary treatment of data systems (i.e., seeing things through a federated-vs-centralized lens). The chapter also proposes enhancements to the "people side" of institutional investors' data systems, including a need for global-local comprehension and coordinated entrepreneurship around organizational data.

This chapter inspects a suite of tech platforms and tools to enable data empowerment for institutional investors. It studies tools for transforming data, enhancing it, and extracting crucial insights from it, including metadata, inference algorithms, data-workflow pipelines, tools for breaking the tyranny of spreadsheets, and visualization utilities. The chapter covers problems that institutional investors have with existing database architectures. It also tours some advanced solutions that could support data-empowerment programs well into the future.

This chapter explains some terminal difficulties with risk-management tools from modern finance. Likewise, it asserts some heretical things about conventional approaches to portfolio diversification, including that it's actually an expensive (not free) lunch for long-term investors and a dangerous starting point for portfolio construction and risk management. The chapter introduces a replacement approach to managing risk that's better geared toward long-term institutional investors, especially ones that are reorienting themselves around advanced technology. The chapter explores how three categories of technology—alternative data, knowledge-management tools, and smart contracts—can put this new approach into action.

This chapter delves into technology that can help institutional investors to more dexterously manage their exposures to risk. It investigates the importance of exposure purity and how technology can assist in cultivating and maintaining it. The chapter explores advanced technologies that can assist institutional investors in pursuing exposure purity via asset allocation, benchmarking, achieving flexible access to investment opportunities, and cooperativity.

This chapter investigates best practices and institutional investors' direct experiences with making innovation and technology both more complementary and programmatic. The chapter studies the lessons learned by some pioneering institutional investors in their attempts at infusing more innovative mindsets, processes, and forcing mechanisms into their organizations. The chapter looks into the essential contributions made by the "softer side" of innovation, in terms of the importance of a culture of learning, ways to preserve enough of the right types of resources to fuel innovation for the long term, and innovation partnerships.

This chapter give an in-depth case study of a revolutionary approach to programmatic innovation: the creation of an in-house task force to drive technological innovativeness. The chapter describe four institutional investors' activities in transitioning to this approach, in terms of design choices they made and frameworks they used to guide their thinking. Further, the chapter discusses implications of this achievement for other institutional investors—namely, as an illustration of what game-changing possibilities lie ahead.

This concluding chapter gives a pep talk for readers on how their organizations can take the very first steps on the road to technologizing—and become true superheroes in so doing. The chapter offers detailed pointers on the initial moves to make when putting this book's lessons into practice. It gives one last rallying cry on how essential it is for institutional investors to collaborate with start-ups, as well as each other, in technologizing and innovating—in order to deliver an overall brighter future for us all.