Hype cycle for emerging technologies

Hype cycle for emerging technologies
Name	Hype cycle for emerging technologies
Caption	Diagrammatic representation of a hype cycle
Introduced	1990s
Field	Technology forecasting

Hpe cycle for emerging technologies

The hype cycle for emerging technologies is a graphical and conceptual tool used to track the maturity, visibility, and adoption expectations of innovations developed by organizations such as Gartner, MIT Media Lab, Xerox PARC, Bell Labs, and Fraunhofer Society. It is referenced in reports by Forrester Research, OECD, World Economic Forum, European Commission, and National Science Foundation and discussed in literature from Harvard Business School, Stanford University, Massachusetts Institute of Technology, Carnegie Mellon University, and Oxford University. Policymakers at United States Department of Commerce, European Parliament, United Nations, Organisation for Economic Co-operation and Development, and World Bank use analogous concepts when evaluating technologies such as artificial intelligence, blockchain, quantum computing, gene editing, and autonomous vehicles.

Overview

The model emerged in industry analysis by Gartner and was influenced by research from Joseph Schumpeter-informed scholars, practitioners at Bell Labs, theorists at RAND Corporation, analysts from McKinsey & Company, and thinkers at Institute for the Future. It maps phases from initial innovation to mainstream adoption, informing strategy at firms like IBM, Microsoft, Google, Amazon (company), Apple Inc. and guiding investment by entities including Sequoia Capital, Andreessen Horowitz, BlackRock, Goldman Sachs, and Venture Capital firms. The cycle is used alongside other frameworks from Boston Consulting Group, KPMG, PricewaterhouseCoopers, Deloitte, and academic programs at Wharton School.

Methodology and Components

Analysts construct a hype cycle using signals drawn from patent filings at United States Patent and Trademark Office, publication metrics from Nature (journal), Science (journal), conference proceedings of NeurIPS, ICLR, SIGGRAPH, CHI Conference on Human Factors in Computing Systems, and citation indices such as Web of Science and Scopus. Data sources also include market reports by IDC, legal filings in United States Securities and Exchange Commission, procurement notices from General Services Administration, and adoption case studies from companies like Siemens, General Electric, Bosch, Toyota, and Tesla, Inc.. Core components are visibility, expectations, maturity, adoption rate, and business applicability, concepts debated in scholarship at London School of Economics, Yale University, Princeton University, and Columbia University.

Stages of the Hype Cycle

The canonical stages—Technology Trigger, Peak of Inflated Expectations, Trough of Disillusionment, Slope of Enlightenment, and Plateau of Productivity—are described in analyst reports from Gartner and critiqued in articles in Harvard Business Review, MIT Technology Review, The Economist, Financial Times, and Wall Street Journal. Each stage is illustrated with historical examples such as early Internet commercialization, the dot-com bubble, 3D printing adoption patterns, the rise and fall of virtual reality startups, and recovery seen in cloud computing deployments. The trajectory has parallels in diffusion models from Everett Rogers and decision frameworks used at US Department of Defense acquisition programs and European Space Agency missions.

Applications and Use Cases

Enterprises use the hype cycle to prioritize R&D at Bell Labs, allocate capital at Blackstone, develop roadmaps at Siemens AG, inform corporate strategy at Procter & Gamble, and align product development at Samsung Electronics. Governments apply it when designing industrial policy at Ministry of Industry and Information Technology (China), technology roadmaps at National Institute of Standards and Technology, and public funding programs at Horizon Europe and DARPA. Academic technology transfer offices at MIT Technology Licensing Office, Stanford Office of Technology Licensing, and Cambridge Enterprise employ the model to manage spinouts linked to CRISPR, graphene, nanotechnology, robotics, and synthetic biology.

Criticisms and Limitations

Critics from Stanford Law School, Yale School of Management, University of Chicago Booth School of Business, and commentators in The New York Times argue the cycle oversimplifies dynamics highlighted by research at Brookings Institution, RAND Corporation, Resources for the Future, and Pew Research Center. Common critiques note its deterministic presentation contrasted with stochastic models in papers from IEEE, ACM, Royal Society, and National Academies of Sciences, Engineering, and Medicine, and its weak empirical grounding relative to econometric analyses used by International Monetary Fund and European Central Bank. Legal scholars at Harvard Law School and Georgetown University Law Center point to potential policy missteps when hype-driven procurement interacts with procurement law in jurisdictions like United Kingdom, Germany, and United States.

Impact on Industry and Policy

The hype cycle influences investment decisions at SoftBank, strategic pivots at Intel Corporation, merger activity monitored by Federal Trade Commission, standard-setting at International Telecommunication Union, and regulatory responses from European Commission Directorate-General for Competition. It shapes public discourse in outlets such as Bloomberg, Reuters, BBC News, Al Jazeera, and Associated Press, and informs legislative inquiries in bodies including United States Congress, European Parliament Committee on Industry, Research and Energy, and national parliaments in Japan, India, and Australia.

Case Studies of Notable Technologies

Representative case studies include the Internet from commercialization through the dot-com bubble and recovery driven by incumbents like Amazon (company) and Microsoft Corporation; blockchain and the 2017–2018 speculative episode associated with companies such as Ethereum foundations and exchanges like Coinbase; autonomous vehicles with deployments and setbacks involving Waymo, Uber Technologies Inc., NVIDIA, Baidu, and Toyota; quantum computing advances at IBM, Google LLC, Rigetti Computing, and D-Wave Systems; and CRISPR gene-editing developments commercialized by startups spun out of Broad Institute and University of California, Berkeley.

Category:Technology forecasting