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In 2015 and 2016, the technology sector was in a full-blown artificial intelligence “Arms Race.” At that point, it was anyone’s game, and the leading technology companies were in a merger and acquisition frenzy. The reality is that this so-called “arms race” in cognitive technologies began long ago between the technology company superpowers—Google, Amazon, Microsoft, IBM, Facebook, and Apple—in the form of investments in strategic R&D. With the enterprise cloud market expected to grow from $70 billion to more than $250 billion by 2017, by 2015 this race escalated into full-blown combat, with machine learning as the battleground.
By 2016, there had been 100 mergers and acquisitions (M&A) within the technology sector involving artificial intelligence companies, products, and services. I was working on research at the time with the objective of better understanding what it all meant. At the time, my co-authors and I characterized the nascent AI marketplace as “in the midst of a real-world paradigm shift: the final stages of a decades-long transition from the scientific discipline known as artificial intelligence(and its various sub-disciplines) into an array of applied cognitive technologies made more widely available through innovative enterprise architectures unique to the business culture of the technology sector.”
Crucial innovations and breakthroughs were a vital precursor to the business consolidation of the AI sector (and its subsectors) via merger and acquisition by the likes of Google, Microsoft, and IBM. What really enabled this transition to widespread commercialization were seminal deep learning and neural network breakthroughs that transformed the discipline – all enabled by software as a service, cloud-based access to structured and unstructured data at a scale that, we now realize, AI was waiting to get its hands on to fulfill its promise. Further convergence of the maturity of other technology capabilities (i.e. computational power) and the democratization of access to powerful software and data systems were now in place. AI capabilities were now structured to sit on top of a powerful cloud-based stack.
On the business side of the equation, AI-based platforms as service offerings (PaaS) became an extension of Total Lifetime Value (LTV) metrics. LTV, ” also known as lifetime value, is the total revenue a company expects to earn over the lifetime of its relationship with a single customer. (1) Yes, AWS and early cloud services are the origin story of this market success. For example, Microsoft Exchange, Office and Azure Enterprise subscriptions are all the same aggregate XaaS business model measured in TLV. TLV allows for the creation of strategic value to the customer as the priority for growth and revenue. As the artificial intelligence marketplace took off, what was surprising is that all the myths around artificial intelligence (fueled by decades of science fiction) never put business strategies like innovative enterprise and technology delivery architectures at the center of the mythmaking.
Development platforms also played a crucial role. Anecdotally, most recently biotech also took a page from the technology sector’s playbook with their mRNA platform. Moderna and Pfizer leadership are not shy in their frequent characterization of the vaccines as “technology platforms.” Technically, McDonald’s and Ford Motors were development platform innovations, including new structures and value propositions for suppliers, wholesalers, and new distribution models and touch points for the end customer (franchisees and car dealerships), all of which were as important to the success of those companies as the quality of the product or the role of technological innovation at the time.
Application programming interfaces (APIs) are where IT professionals and AI experts found common ground. Machine learning APIs were readily available through both AWS and Microsoft Azure Marketplace, while the IBM Watson-powered API Harmony service on Bluemix allows developers to search publicly available APIs based on wide-ranging search criteria. Companies found opportunities to provide support for developing and applying cognitive technologies—and APIs were the form of and the building blocks of these new products and services.
Put simply: artificial intelligence always needed platforms and ecosystems at scale to really work – democratized, vast amounts of data, structured and unstructured, with network effects and exponentiality. Quantum is not where AI was in 2015/2016, but the release by OODA CTO Bob Gourley of The Quantum List: Companies leveraging quantum effects for real-world functionality and security comes with the realization that the way quantum is positioned for success best illustrates how exponential technologies are now working in real-world practice. Autonomous operating systems, for example, and (the focus of this post) quantum technologies will also be enabled by this enterprise architecture on which the technology sector is now built. Over time, the quantum technology marketplace will be populated with new business models, development platforms, and platform as service offerings.
At some point, like artificial intelligence, quantum will be “in the midst of a real-world paradigm shift: the final stages of a decades-long transition from the scientific discipline known as quantum computing (and its various sub-disciplines) into an array of applied quantum computing technologies made more widely available through innovative enterprise architectures unique to the business culture of the technology sector.” Equally as interesting is that everything is now in place to continue to transform consecutive scientific disciplines (at exponential speed and scale) into the marketplace of solutions for the next few decades.
Exponential speed and scale have started to lose their ‘pie in the sky’ strategic implications and, based on the insights garnered from The Quantum List, are feeling progressively more tangible and tactical with each passing year. What we all intuited (when we first heard of and evaluated the promise of exponential technologies over the last eight to ten years or so, ) is that the next few decades could be (hands-down) one of the most breathtakingly transformative periods in human history.
What inspired this discussion and this post is, upon a review of The Quantum List, it was uncanny how many vital enterprise architecture innovations are already integrated into the list. While characterized as capabilities in the intro to the list, the four categories which make up The Quantum List (Quantum Computing, Quantum Security, Quantum Communications, and Quantum Sensing) can also be thought of (and may emerge as) quantum computing subsectors, new markets or, for companies of a certain scale, a new business units. The quantum organizations on the list from the likes of Google, Microsoft, and IBM are consciously designed as an extension of the successful elements of the enterprise architecture innovation they deployed during the recent AI Arms Race.
Other examples include: Rigetti Computing is a full-stack quantum computing company…the company develops software to integrate its systems directly into existing cloud infrastructure; Arqit develops a quantum encryption platform; and SandboxAQ is an enterprise SaaS company that provides AI and quantum computing solutions. SandboxAQ’s solutions include post-RSA cybersecurity modules that migrate enterprises to higher levels of security. Conversely, the description of Quantum Xchange – “a solution for secure communications based on quantum effects. Its complete key management system supports both post-quantum crypto (PQC) and Quantum Key Distribution (QKD). The operator of the first quantum fiber network in the U.S.” – feels a bit closed garden and proprietary. Cold Quanta is investing in a portfolio of quantum capabilities including a Cold Atom quantum computer. The portfolio of solutions includes lab devices, quantum positioning, and telemetry. Is Cold Quanta designed as an Exponential Organization?
The future of quantum computing will be built based on a tested and proven framework for the design of a technology ecosystem (and the structure of future quantum companies) built to sustain the exponential scale and speed of this, our current technological and scientific era. Exponentials are not futurist high-level concepts. They are not an intellectual bug, but the central organizing feature of the quantum computing road ahead.
Your organization should be exploring how to create value propositions, quantify risk and create institutional learnings by partnering with companies from The Quantum List directly – or, as they emerge, participating in the broader quantum computing ecosystem, development platforms, and PaaS opportunities.
Following are the working definitions of the business strategy, business design, technology-based business models, and value proposition design innovations that emerged from the AI marketplace research at Deloitte in 2016 (“Cognitive technologies inthe technology sector: From science fiction vision to real-world value“):
Moonshots: Projects that aspire to exponential, tenfold improvements [1,000 percent increases] in performance.
Exponentials: Moonshots are part of an emergent business strategy framework known as “exponentials,” which is based on the exponential acceleration of technologies such as quantum computing, artificial intelligence, robotics, additive manufacturing, and synthetic or industrial biology, amongst others. These and other exponential technologies are creating new competitive risks and opportunities for enterprises that have historically enjoyed dominant positions in their industries.
In a very accessible manner, Salim Ismail (the founding executive director of Singularity University and former head of innovation at Yahoo) explains the doubling pattern and trajectory which further accelerate Moore’s Law (contributing to the exponential growth of an innovation) in the following way:
Ismael also mentions Google Ventures (which will continue on as the venture capital business unit of the Alphabet holding company) as “an almost perfect exponential organization.”
Exponential Organizations (ExOs): An organization whose impact (or output) is disproportionally large, at least 10 times larger, compared to its peers because of the use of new organizational techniques. Google is No. 5 on the Top 100 ExOs list, along with companies such as Airbnb, Uber, Tumblr, Medium, and Twitch. At No. 1 is the collaborative code and software development site GitHub. Alphabet is making 10x growth a business priority supported from inception by the new company’s enterprise architecture.
Alphabet is, by design, an exponential organization (ExO). It was not surprising that the re-organization of Google into the Wall Street-friendly Alphabet happened right in the middle of the AI M&A wars in the 2015/2016 timeframe. The same design elements of Alphabet that allow for Google, Google X, YouTube, and recent cybersecurity acquisition Mandiant to be in the same portfolio of Alphabet companies also allowed for AI acquisitions and will allow for quantum computing bets to also join the Alphabet portfolio.
Business Ecosystems: A complex, dynamic, and adaptive community of diverse players who create new value through increasingly productive and sophisticated models of both collaboration and competition.
Quantum Business Model Transformation: New business units are created to increase volume and grow revenue through quantum computing-enabled innovation. The most striking finding is that technology companies create new business units to increase volume and generate revenue by using the rapidly commercializing technology innovation not only for product innovation but also for structural, operations, process, and business model innovation as well. Change at this speed and scale creates opportunities—and risk—which will challenge strongly held beliefs at the core of a company’s business model. These new units are also designed to transform the architecture of the parent company over time. Such restructuring underlines quantum computing’s potential to completely revolutionize the technology sector—and take many vertical industries and markets along.
Multisided Platforms (MSPs): Technologies, products, or services that create value primarily by enabling direct interactions between two or more customer or participant groups. See Andrei Hagiu, “Strategic Decisions for Multisided Platforms,” (MIT Sloan Management Review, December 19, 2013).
Quantum Development Platforms: Platforms— which are increasingly supported by global digital technology infrastructures that help to scale participation and collaboration— help make resources and participants more accessible to each other. Properly designed, they can become powerful catalysts for rich ecosystems of resources and participants, defining the protocols and standards that enable a loosely coupled, modular approach to business process design. Quantum developer platforms are emerging that allow organizations to collaborate with a community of passionate developers, which accelerates the speed and scalability of product development and product release efforts.
Quantum Platform-as-a-Service Offerings (PaaS): Modular, extensible products are redesigned for the computing-intensive demands of quantum computing, allowing both current and new customers to easily transition to PaaS offerings – as well as companies to rapidly position their PaaS offerings in new markets. Again, an example from recent AI market history: In the case of Amazon, machine learning as service dates back to the company’s early offerings of infrastructure as a service (IaaS) and software as a service (SaaS) through Amazon Web Services (AWS). Amazon’s launch of a machine learning-based PaaS offering can be seen as a direct result of its significant, long-term strategic research and development investments, notwithstanding the criticism these investments attracted from investors and Wall Street.
Quantum PaaS Extensions: PaaS vendors provide a virtual IT environment that allows businesses to develop and run their own customized applications without having to manage the details of a physical or cloud-based data center. Many companies will enhance current PaaS offerings through modular, extensible products designed for quantum technologies’ computing-intensive demands. This approach allows a company’s current and new customers to easily transition to the PaaS offering and enables the company to rapidly position its quantum computing PaaS offerings in new markets. The AI business ecosystem, the IBM Watson Group, is illustrative: Watson Services on Bluemix is an open-standard, cloud-based PaaS for building, managing, and running applications of all types, such as mobile, big data, and new smart devices, providing a fully integrated service for cognitive technology innovation. In the case of early AI PaaS extensions, typically these platforms and PaaS offerings target high-value, immediately addressable markets such as analytics, cloud computing, social, mobile, and security. It will be interesting to see if quantum computing targets these same markets, or if new high-value immediately addressable markets eclipse these current markets and/or are more value-driven in a quantum innovation context.
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About the Author
Daniel Pereira is research director at OODA. He is a foresight strategist, creative technologist, and an information communication technology (ICT) and digital media researcher with 20+ years of experience directing public/private partnerships and strategic innovation initiatives.
Informing your decisions with actionable intelligence
Informing your decisions with actionable intelligence