Two of the most important computer science principles in the technology industry are abstraction and platformization. Our aim with this note is to explain these concepts in an approachable way that brings managers and computer scientists a little closer together.
Generative AI seemed poised to reshape the world of work, including the higher-wage, white-collar jobs typically pursued by MBA graduates. Informed by the latest research, this case explores generative AI's potential impacts on work, productivity, value creation, and the labor market.
By 2023, the global market for cloud infrastructure had consolidated into a three-horse race. As of Q4 2022, Amazon, Microsoft, and Google collectively accounted for 66% of the global market. AWS had a market share of 33%, Microsoft Azure had 23%, and Google Cloud had 11%. The global market for cloud services, valued at over $550 billion in 2023, was projected to grow at an annual rate of 15-20%, reaching over 2 billion by 2030. Though the growing demand for cloud services was a reassuring tailwind for Microsoft, the emergence of multicloud approaches in enterprise, the relentless price-pressure from competitors, and the threat of further commodification presented a wholly new set of challenges for Microsoft Azure. As the opportunities and threats of the multicloud future became clearer, Microsoft needed a multicloud strategy.
Generative AI refers to a type of artificial intelligence (AI) that can create new content (e.g., text, image, or audio) in response to a prompt from a user. ChatGPT, Bard, and Claude are examples of text generating AIs, and DALL-E, Midjourney, and Stable Diffusion are examples of the image-generating variety. During training, a generative AI learns the underlying structure of the desired output by absorbing a mass of relevant data - all of the books in the public domain, for example, or petabytes of text scraped from across the internet. Once trained, generative AIs work by creating outputs that recreate, with calculated variation, the underlying patterns learned in training. In 2023, all these types of generative AI were created in a similar process. At the core of any generative AI system is the model, a mathematical representation of patterns that forms the basis of 'knowledge' for the system. The structure of the model is determined by its architecture, the theoretical organization of parameters in an artificial neural networks that the system uses to generate its outputs. To learn, the model relies on a mountain of training data, a collection of examples relevant to the task the model is being trained to perform. During an initial pre-training process, the model learns to adjust its parameter-weights (assumed by the architecture), improving its prediction quality with many iterations over time; that model is further refined through a fine-tuning process. Training an AI system requires specialized hardware, like GPUs in data centers, that consume enormous amounts of electricity to handle heavy and massively-parallel computational loads.
In February 2024, the world was looking to Google to see what the search giant and long-time putative technical leader in artificial intelligence (AI) would do to compete in the massively hyped technology of generative AI. Over a year ago, OpenAI released ChatGPT, a text-generating chatbot that captured widespread attention. OpenAI would offer a range of new generative AI products as both user-facing applications and developer-facing application programing interfaces (APIs). In January 2023, Microsoft and OpenAI signed a $10 billion deal extending their exclusive partnership. Microsoft would continue to supply OpenAI with seemingly unlimited computing power from its Azure cloud, and Microsoft hoped that OpenAI's technology and brand would keep Microsoft at the center of the new generative AI boom. Microsoft announced that it would soon begin deploying OpenAI's technologies throughout its suite of products, from its Microsoft 365 productivity apps to its search engine Bing. Google needed to decide how to respond to the threat posed by OpenAI and Microsoft. Google had a decade of experience developing and deploying AI and machine learning (ML) technologies in its products, but much of their AI work happened in-house and behind the scenes. Google researchers had invented the transformer architecture that made the generative breakthroughs demonstrated by GPT possible. Breakthroughs in AI had been quietly supercharging Google products like Search and Ads for years, but most of the product work was internal and little of it had penetrated the public consciousness. Until 2022, Google leadership had been deliberately cautious about revealing the extent of their AI progress and opening Google's experimental AI tools to the public.
In 2023, the term metaverse - a combination of "meta" and "universe" - had become a catch-all for a diverse set of expectations about online virtual worlds and the future of the internet. To some, the metaverse conjured images of a massive participatory videogame inspired by science fiction. To others, the metaverse meant the evolution of the internet into something more three-dimensional and social. In October 2021, Facebook CEO Mark Zuckerberg announced that his strategy would be "metaverse-first," leading him to change Facebook's name to Meta. However, executives at other companies like Epic Games, Microsoft, Nvidia, Electronic Arts, and Apple had different views of if, when, and how the metaverse would take shape. Amid the hype and uncertainty, executives and entrepreneurs had to grapple with critical questions as they strove to form their own vision and strategy for the metaverse. First, was the metaverse going to emerge in the next few years or much further down the road, if at all? Second, what would be the important use cases? Some expected gaming to emerge first, while others expected enterprises would drive adoption. Third, and perhaps most critically, would the metaverse be an open and interoperable virtual world, like the internet itself? Or would the development of the metaverse play out like the more recent models of app stores and social networks, born on the internet but maintained as distinct walled gardens? Answers to these questions would shape billions of dollars of investment, profits, and losses.
In business and strategy contexts, network effects are often accompanied by bandwagon (or herding) effects, positive feedback loops (or accumulated advantage effects), and market tipping (or winner-take-all dynamics). Though these phenomena are often grouped together under the general use of the term "network effects," this note aims to distinguish the core notion of network effects from the related phenomena that interact and often co-occur.
In 2022, Cher Wang, CEO and Chairwoman of HTC, was focused on the company's pivot to virtual reality and the metaverse. Growing competition in consumer virtual reality from Meta, Sony, and Chinese headset manufacturers had altered the competitive landscape since 2017. This supplement updates the case "HTC and Virtual Reality" (718-421).
This B case, set in summer 2022, was designed as a companion to "Netflix: A Creative Approach to Culture and Agility," a case set in 2018. The purpose of this brief document is to unlock a discussion around how the Netflix culture can be used to weather new challenges facing the company: rising competition, economic contraction, and declining subscribers.
Applied Intuition, a leader in autonomous vehicle simulation software, has just closed on a $175 million round of Series D financing that values the four-year-old firm at $3.6 billion. With the immediate future secure, CEO Qasar Younis must now chart a strategic course for Applied Intuition's growth over the next ten years.
Direct-to-consumer (DTC) brands such as Allbirds, Casper, Peloton, and Warby Parker have creatively found a weakness in the marketing citadel of incumbent brands. By using data gleaned from daily interactions with customers, these brands have been able to adapt how they serve their unique customer communities across a start-to-finish purchase journey. The best of them have parlayed that ability into a profitable business model applied across multiple channels and customer segments. But as successful DTC brands mature, they must recognize the need to evolve. The authors offer four principles for continued success: (1) Focus on deepening customer relationships, not just making comparisons with competitors. (2) Accompany the customer beyond the initial transaction. (3) Omnichannel is about value addition, not cost reduction. (4) Strengthen the core first; consider extensions later.
The leader of a pioneering biotech company in the siRNA space weighs his options for scaling production capacity in advance of an anticipated commercial launch. Operational complexity and relative merits of in-house manufacturing versus a contractor model are discussed.