Emerging technologies
Technologies whose development and applications are still largely unrealized.
Emerging technologies are innovations that haven’t yet been fully developed or put into widespread practical use. They can be brand new, but sometimes they’re older technologies that have found fresh applications. People often see them as having the potential to shake up the way things are done. What sets them apart is their radical novelty—even if the underlying idea isn’t new—along with fast growth, a clear direction, a big impact, and a lot of uncertainty and ambiguity. These technologies span fields like information technology, nanotechnology, biotechnology, robotics, and artificial intelligence.
Sometimes, new technological fields emerge when different systems converge—that is, when separate technologies evolve toward similar goals and start sharing resources. For example, voice (telephone features), data (productivity software), and video have come together to interact and create new efficiencies. Emerging technologies are progressive innovations that give a competitive edge, while converging technologies are previously separate fields that are becoming more interconnected and aligned in purpose. Still, opinions differ on how much impact these technologies will have, how viable they are economically, and what their true status is.
**History of emerging technologies**
Throughout the history of technology, emerging technologies have been the latest advances and breakthroughs in various fields. Over centuries, new methods and technologies have appeared, some born from theoretical research and others from commercial research and development. Technological growth includes both incremental improvements and disruptive changes. An example of an incremental development is the DVD, which was a gradual upgrade from the compact disc. In contrast, disruptive technologies replace older methods entirely, like automobiles making horse-drawn carriages obsolete.
**Emerging technology debates**
Many writers, including computer scientist Bill Joy, have pointed to clusters of technologies they believe are critical to humanity’s future. Joy warns that elites could use these technologies for good or evil—acting as “good shepherds” for the rest of us, or deciding that most people are unnecessary and pushing for mass extinction. On the other hand, advocates of technological change see emerging and converging technologies as a way to improve the human condition. Cyberphilosophers Alexander Bard and Jan Söderqvist argue in *The Futurica Trilogy* that humans themselves are basically constant (genes change slowly), while all meaningful change comes from technology (memes change fast), because new ideas always arise from technology use, not the other way around. They say humans are history’s main constant, and technology is its main variable. But critics, and even some advocates like transhumanist philosopher Nick Bostrom, warn that some of these technologies could be dangerous, possibly even leading to human extinction—what they call existential risks.
Much ethical debate focuses on distributive justice: who gets access to beneficial technologies. Some thinkers, like environmental ethicist Bill McKibben, oppose further development of advanced technology partly because they fear its benefits will be shared unequally, making the poor worse off. In contrast, inventor Ray Kurzweil is among techno-utopians who believe emerging and converging technologies could eliminate poverty and suffering. Other analysts, like Martin Ford, argue that as information technology advances, robots and automation will cause significant unemployment as machines and software match or exceed human workers in most routine jobs. As robotics and artificial intelligence improve, even skilled jobs could be threatened. Technologies like machine learning might eventually let computers handle many knowledge-based jobs that require education. This could lead to widespread unemployment at all skill levels, stagnant or falling wages, and more wealth concentrated among capital owners. In turn, that could depress consumer spending and economic growth, since most people wouldn’t have enough disposable income to buy what the economy produces.
**Examples of emerging technologies**
**Artificial intelligence**
Artificial intelligence (AI) is the intelligence shown by machines or software, and the branch of computer science that creates machines and software with animal-like intelligence. Major AI researchers and textbooks define the field as the study and design of intelligent agents—systems that perceive their environment and act to maximize their chances of success. John McCarthy, who coined the term in 1956, defines it as the study of making intelligent machines. The main goals of AI research include reasoning, knowledge, planning, learning, natural language processing (communication), perception, and the ability to move and manipulate objects. General intelligence, or strong AI, remains a long-term goal. Currently, popular approaches include deep learning and statistical methods.
- field
- Technology
- known_for
- Radical novelty, fast growth, prominent impact, uncertainty
- key_characteristics
- Radical novelty, relatively fast growth, coherence, prominent impact, uncertainty and ambiguity
- examples
- Information technology, nanotechnology, biotechnology, robotics, artificial intelligence
Lore & Background
In the history of technology, emerging technologies are contemporary advances and innovation in various fields. Over centuries, innovative methods and new technologies have been developed through theoretical research and commercial research and development. Technological growth includes incremental developments, such as the gradual roll-out of DVD as a follow-on from compact disc technology, and disruptive technologies, where a new method replaces the previous technology, like automobiles replacing horse-drawn carriages.
Reader's Guide
Emerging technologies are significant because they are perceived as capable of changing the status quo and often involve convergence—previously separate technologies moving towards stronger interconnection and similar goals, creating new efficiencies. Debates surround their impact: some writers, including computer scientist Bill Joy, warn that elites could use them for good or evil, potentially causing mass extinction. Advocates see hope for betterment of the human condition, while critics warn of existential risks. Ethical debates center on distributive justice, with some fearing unequal benefits that worsen poverty, and others believing they could eliminate poverty. Analysts like Martin Ford argue that automation from AI and robotics may lead to significant unemployment and increased concentration of wealth. The legacy of emerging technologies is thus marked by both promise and profound uncertainty.
Did You Know?
- Emerging technologies include both new technologies and old technologies finding new applications.
- Converging technologies represent previously distinct fields moving towards stronger inter-connection and similar goals.
- Bill Joy identified clusters of technologies he considered critical to humanity's future.
- Martin Ford argued that robots and automation could result in significant unemployment at all skill levels.
Origins and the Quest for General Intelligence
Founded in November 2010 by Demis Hassabis, Shane Legg, and Mustafa Suleyman, DeepMind was born from a shared ambition to build artificial intelligence capable of tackling virtually any problem. Hassabis and Legg first crossed paths at the Gatsby Computational Neuroscience Unit at University College London, where their research sensibilities aligned around the idea of general-purpose cognition rather than narrow, task-specific systems. The early team put that philosophy to work by teaching neural networks to play 1970s and 80s Atari titles—Breakout, Pong, Space Invaders—without any hand-coded rules. The systems initially flailed, then gradually discovered strategies, mirroring how a human novice might learn a game from scratch. Backed by investors including Horizons Ventures, Founders Fund, Peter Thiel, Elon Musk, and adviser Jaan Tallinn, the lab attracted attention from multiple tech giants. On January 26, 2014, Google confirmed its acquisition for a reported $400 to $650 million, giving DeepMind the compute and resources to scale its research ambitions.
Conquering the Board and the Arcade
DeepMind's most celebrated public moment arrived in 2016 when AlphaGo defeated Go world champion Lee Sedol in a five-game match, an event later captured in the documentary AlphaGo. But the company's game-playing ambitions stretched far beyond a single board. Its reinforcement-learning architectures, trained on raw pixel input without programmed game knowledge, had already surpassed human performance in Atari classics by 2013. In 2018, researchers extended the approach to the 3D first-person shooter Quake III Arena, and in 2020 the Agent57 system pushed into even more complex territory. The general-purpose philosophy set DeepMind apart from predecessors like IBM's Deep Blue, which was engineered for one specific game. AlphaZero later outperformed the strongest dedicated programs in Go, chess, and shogi after merely days of self-play. More recent entries—MuZero, AlphaStar, AlphaGeometry, FunSearch, AlphaEvolve, AlphaDev, and AlphaTensor—demonstrate how the same learning principles keep expanding into mathematics and algorithm discovery.
From Protein Folding to a Thousand Papers
Beyond games, DeepMind has made profound inroads into scientific problem-solving. In 2020, its AlphaFold system achieved state-of-the-art results on protein-folding benchmark tests, and by July 2022 the company announced the release of over 200 million predicted protein structures—effectively covering virtually every known protein—onto a public database. This work sits alongside a broader research output: as of 2020, the lab had published more than a thousand papers, including thirteen accepted by Nature or Science. The company's media footprint reflected its growing influence; a LexisNexis search found 1,842 news stories referencing DeepMind in 2016 alone, a figure that eased to 1,363 by 2019 as the AlphaGo spotlight faded. The breadth of its scientific contributions, from structural biology to algorithmic discovery, underscores a lab that treats AI as a general-purpose tool for accelerating human knowledge rather than a single-product venture.
Generative AI, Ethics, and a New Chapter
By the early 2020s, DeepMind's portfolio had expanded well past games and science. The lab became responsible for two large language model families—the proprietary Gemini and the open-weight Gemma—alongside generative models such as Imagen for text-to-image, Veo for text-to-video, and Lyria for text-to-music. The pace of the generative-AI race prompted a structural shift: in April 2023, DeepMind merged with Google's Google Brain division to form Google DeepMind, a move partly driven by the public release of ChatGPT and partly the culmination of years of internal tension over autonomy. Ethical governance had been a recurring theme; after the 2014 acquisition, the company established an AI ethics board, later formalized as DeepMind Ethics and Society with philosopher Nick Bostrom as an adviser. The lab also pursued societal applications, signing a 2015 information-sharing agreement with the Royal Free NHS Trust to co-develop a clinical task-management app called Streams. In the summer of 2026, the team relocated into Google's Platform 37 building in King's Cross Central, London.
Gallery
More in Miscellaneous 1-24
Spotted an error? Know more?
This is a living reference — every entry is fact-audited, and reader corrections feed straight into our audit queue. Suggest an edit · See this site's audit record