The convergence of technologies is driving forward innovation, new approaches, new production methods, new applications and new governance challenges. Four important technology areas – synthetic biology, neurotechnology, quantum technologies and earth observation from space – illustrate these processes. While technology convergence can be understood in terms of products or technology applications, it can also be understood as a process of integration, not only across technologies, but also disciplines and communities. Policymakers around the world are enabling convergence by designing “convergence spaces”: institutions and programmes that integrate scientific approaches, technical infrastructure and interdisciplinary skill sets. The intention is to unleash the generative potential of deep multidisciplinarity and interdisciplinary assets. The discussion of the four technology areas reveals the possibilities of convergence as a generative force in each domain and points to new challenges and opportunities for emerging technology policy.
OECD Science, Technology and Innovation Outlook 2025
Abstract
Technology convergence can be understood in terms of products or technology applications, but also as a process of integration involving different disciplines and communities. Societal transformations will require harnessing the dynamism of technology convergence, a trend emerging with particular force in the contexts of artificial intelligence (AI), neurotechnology, synthetic biology, quantum technology and space observation.
Convergence can be enhanced by designing “convergence spaces” – physical, digital and technological infrastructures and platforms that promote the integration of tools, fields and human expertise. Integrating considerations of a regulatory, ethical, legal and social nature can also help shape the outcomes of convergence so that innovation accords with existing regulations and societal values and is sensitive to concerns and risks.
In the interest of promoting technology convergence to drive transformative change in the economy, governments could:
Design convergence spaces through good institutional and programme design to foster deeper forms of interdisciplinary research, engineering and innovation.
Simultaneously consider technological and regulatory developments, since the often-complex regulatory implications of convergence calls for including ethical, legal and social analysis in the interdisciplinary mix of the convergence space.
Analyse the feasibility and potential effects of technological convergence on key sectors, with input from labour and business stakeholders and other representatives of civil society.
Leverage different funding models, access rules and technology transfer structures to shape the technological and collaborative platforms necessary for convergence.
Find agile regulatory approaches and promote strategic intelligence to better anticipate and engage the drivers and impacts of convergence, drawing on the OECD Framework for Anticipatory Governance of Emerging Technologies.
Societal transformations will require harnessing the dynamism of technological convergence, a trend long noted across science and technology policy communities but emerging with particular force in the contexts of AI and the digital transformation, advanced biotechnologies, and materials science. In many analyses, technology convergence promises powerful synergies to enhance the speed and functionality of technologies, tools and products. The phenomenon is seen across academia and industry. Indeed, seeking to expand their existing knowledge domains, many industrial actors are moving beyond traditional single-technology development models in favour of more multivalent cross-disciplinary technology convergence (Ma and Wu, 2024[1]). In particular, AI, as a broadly enabling technology, promises to supercharge the large‑scale integration of digital technologies. Driven particularly by the digital transformation and AI, but not only so, many key areas of emerging technology like robots that learn, biotechnology, quantum science and technology, or satellite systems have become the loci of powerful integration of tools, approaches, disciplines and technologies.
Perhaps because convergence presents special opportunities and challenges for innovation policy that arise with the synergy of hitherto separate domains, it has become one of the central themes of technology and innovation strategies (Sick and Bröring, 2022[2]). Technological integration is giving rise to unique policy dimensions and governance challenges that must be addressed should technologies achieve their full potential, but the empirical basis for policy approaches, while growing, is arguably still limited.
This chapter examines the phenomenon of technology convergence in the context of four important emerging technological areas: synthetic biology, neurotechnology, quantum technologies and space-based earth observation (EO). The combination of technologies previously understood as distinct are driving forward innovation, new approaches, new production methods, new applications and new governance challenges. This chapter argues that while technology convergence can be understood in terms of products or technology applications, it can also be understood as a process of integration not only across technologies but across disciplines and communities. This process has internal logics and technological drivers. Nevertheless, policymakers are enabling convergence by constructing what might be called “convergence spaces”: institutions and programmes that integrate scientific approaches, technical infrastructure and interdisciplinary skill sets. Through its discussion of convergence in the four emerging technology areas, the chapter illustrates the diverse products that are arising at the intersection of multiple technologies, as well as the role of convergence spaces. Convergence phenomena occurring through and around these four technologies are giving rise to new challenges and opportunities for emerging technologies’ policy.
Convergence as a product
Technological convergence is an umbrella term whose definition has grown since its first popularisation in the early 2000s. “Technological convergence” was first used to describe the combination of nanotechnology, biotechnology, information and communication technologies, and cognitive technologies, leading to products such as micro-electro-mechanical systems (used in sensors and actuators from automobiles to electronic game consoles and cellphones), computerised genomics and nanoelectronics. In this usage, technology convergence can be understood in terms of the combination or hybridisation of one or more technologies (OECD, 2014[3]). Today, key convergence products include brain-computer interfaces (BCIs), quantum‑enhanced biosensors and space-based biomanufacturing devices, all emerging from the interaction of synthetic biology, neurotechnology, quantum technologies, space technologies and AI. In this sense of the concept, convergence is primarily located in products, i.e. the resulting technologies themselves. These convergence products are conceived for immediate customer, consumer and patient use or as research tools.
Convergence as a process
Although convergence as tool is a familiar framing that captures key aspects of the phenomenon, convergence can also be understood as a process – dependent on human agency, technologies and systems – that can result in new products, industries, and fields of research and development (R&D). The creation of convergence products results from various cross‑disciplinary and cross-sectoral integration efforts. In this sense, technological convergence refers to a set of processes, typically subdivided into co-evolution and fusion. Co‑evolution is the process by which various technologies develop in tandem, each propelling the other’s advancement. For example, AI is made increasingly powerful with advances in computing hardware, while 5G networks amplify the scale and speed at which AI-driven services can be delivered.
Fusion goes beyond combination and refers to the full integration of scientific knowledge and tools leading to entirely new fields of innovation. In 2014, experts working through the US National Academies defined the concept in terms of combining disciplines to create new fields (US National Academies, 2014[4]). In other words, it “comprises the merging of ideas, approaches, and technologies from widely diverse fields of knowledge at a high level of integration. [This constitutes] one crucial strategy for solving complex problems and addressing complex intellectual questions under emerging disciplines” (US National Academies, 2014, p. 20[4]).
The process has been described not as two parts creating a single whole but rather as Brew describes, “disciplines are more like water than land in that they can be separated yet come together, can combine, merge and recombine in an almost infinite number of ways” (Brew, 2007[5]). Fusion can happen at varying degrees of depth, from full communication infrastructure and networks to data sets and analytics to down-the-line user-facing products and services.
The human element of convergence figures in the design, application of expertise and judgment, institutional context, and framework conditions for enabling development and diffusion. In perhaps its most expansive view, the convergence process has been understood as a phenomenon of “escalating and transformative interaction among seemingly distinct scientific disciplines, technologies, communities, and domains of human activity” (Roco et al., 2013[6]). In this sense, convergence is a true collaboration between human creativity and technological logics (Bijker, Hughes and Pinch, 1987[7]). In addition to reminding us of the inherent social nature of technological change, this conception also creates the possibility for normative integration that crosses over the technology and social divide. For instance, convergence can help embed norms like “privacy-by-design” in technologies and drive responsible innovation.
Convergence spaces and interdisciplinary assets
On some level, technology convergence seems to happen organically and be determined by purely technological logic. But sociologists of science and technology have demonstrated the role of human agency in science and innovation for decades (Latour, 2005[8]). How can governments and other funders help create good conditions to help drive convergence and direct it towards key science, engineering and societal problems?
Creating the right conditions for convergence can depend on the creation of the optimal conditions for R&D institutions, infrastructural assets, human skill sets, and networks to create synergies and novel sciences and technologies – i.e. “convergence spaces”. The OECD has defined convergence spaces as physical and/or material loci that bring together diverse elements – actors, disciplines and technology – in ways that foster convergence (Winickoff et al., 2021[9]). Convergence spaces are akin to “innovation ecosystems”. However, whereas the notion of innovation ecosystem focuses on networks of institutions, the notion of convergence spaces emphasises the goal of integrating scientific and engineering approaches, skills and understandings to draw on and create deep interdisciplinarity. Here the intention is to unleash the generative potential of interdisciplinary assets convened in convergence spaces. In doing so, convergence spaces can produce new kinds of value, products, technologies, training and approaches to regulation. If designed correctly, these can help optimise tangible and intangible value, realise sustainability models, foment collaboration, and promote technological integration. Box 5.1 describes one example of this intent.
Israel’s National Bioconvergence Program is strategically designed to cut across sectors and disciplines bringing together biology, engineering and computational science, and driving innovation in health, agri‑food, manufacturing and the environment. Launched in 2022, the initiative combines multiple public offices spanning science, technology and innovation; health; finance; defence; and academia – broadly aligning policy and ecosystem support. Planned public investment in the programme is ~USD 400 million over a decade, attracting ~USD 200 million more through private sector leverage, including international partnerships (e.g. an international bioconvergence challenges programme). The programme has a five-pillar structure:
1. Research: National funds invest in multidisciplinary applicative research, supported by high‑end research infrastructure and services.
2. Interdisciplinary research and development (R&D): Over USD 80 million publicly invested in industrial R&D, start-ups and consortia, closing funding gaps and growing the ecosystems with dozens of new companies. Key themes include biochips, engineered tissues, bioplastics and circular bioeconomy solutions.
3. Infrastructure: Significant public investments to establish self-sustained R&D service labs supporting innovation in SynBio, precision fermentation and scale-up, and prototyping and small-scale production of biochips/devices.
4. Human assets: Over USD 2 million allocated to various programmes for multidisciplinary training, upskilling and team-building across the academic and industrial pipeline, reaching more than 1 000 people.
5. Enabling regulation: A facilitative regulatory environment developed with the Ministry of Health to guide companies through regulatory pathways for complex bioconvergence health and food technologies, resulting in the world’s first approvals for alternative milk and cultured beef.
Source: Israel Innovation Authority, https://innovationisrael.org.il/en/article/bio-convergence-israels-next-growth-engine.
As a simplified model of the process, three aspects of technological convergence (enabling technologies, fields of R&D, and diverse technical expertise) can be thought of as working through a churning wheel (Figure 5.1), where the process of integrating multiple disciplines and knowledge infrastructures takes place through spaces of collaboration and exchange. Technology convergence can respond to defined market or societal needs to enable the emergence of new industries or research domains. This process can help produce new and hybrid products, open novel research fields, and create new industrial opportunities.
Note: R&D: research and development.
This section examines convergence in the context of four important technological areas: 1) synthetic biology; 2) neurotechnology; 3) quantum technologies; and 4) EO from space. The discussion of the four technology cases reveals the rich possibilities of convergence as a generative force in each domain. In doing so, the chapter illustrates convergence in both its forms: the diverse products that are arising at the intersection of multiple technologies and examples of convergence spaces illustrated in the boxes below.
Convergence in synthetic biology
Synthetic biology brings an interdisciplinary engineering approach to biotechnology, associating biology, digitalisation, engineering, AI and automation. While there is no internationally recognised definition, synthetic biology designs, fabricates, scales and embeds biological components and systems – especially stretches of DNA – into useful applications.1 Synthetic biology has an overarching perspective of harnessing living systems in research, product development and commercial solutions. It involves engineering living systems at multiple scales, from molecules to organisms, to enable research and product development in areas such as chemical, new materials and bio-based fuels. Synthetic biology draws on an array of scientific and technological approaches and tools, as described below, and its convergence finds the most tangible expression in the pieces of infrastructure known as biofoundries, which are interdisciplinary assets par excellence.
Understanding and designing biological systems and protein structure
While synthetic biology has been an important field of science and engineering that predates the rise of AI, its convergence with AI tools and accompanying digital technologies and automation is accelerating the pace of innovation in the field. The combination of synthetic biology and AI, and in particular machine learning, is proving to be a powerful tool for the design and optimisation of biological systems. Machine learning algorithms can be used to analyse vast amounts of experimental data, such as gene expression profiles and metabolic fluxes, to identify patterns and predict the behaviour of biological systems (Vindman et al., 2024[10]). By training deep neural networks on large data sets of genotype-phenotype relationships, researchers can create models that accurately predict the outcomes of genetic modifications, such as gene knockouts or mutations. These models can then be used to guide the rational design of engineered organisms, reducing the need for trial-and-error experimentation and guiding the design of new genetic circuits, metabolic pathways and synthetic organisms with desired properties (Iram, Dong and Ignea, 2024[11]). These approaches have a wide range of applications across both research and economic sectors, including the use of computational prediction tools.
Computational prediction tools can provide new or faster information to researchers to help accelerate research. Understanding protein structures at the molecular level is a foundational biotechnology innovation, particularly in the development of new therapies. Recently, AI tools like deep learning have been shown to help predict protein structure with the same accuracy as experimental methods. Google DeepMind’s AlphaFold, which contributed to the awarding of a shared Nobel Prize in Chemistry, is a prominent example of how convergence is being successfully applied in synthetic biology today. By leveraging publicly available data on known protein sequences and structures (obtained experimentally), the AI model can accurately and efficiently predict the 3D structure of proteins based solely on their amino acid sequence. Their latest model, AlphaFold 3, can go further and predict the interactions and structures between proteins and other biomolecules, like DNA and RNA. Traditionally, these analyses of proteins were time‑consuming and expensive, a major bottleneck to innovation.
Chemicals and materials
AI-augmented synthetic biology is also poised to transform the production of materials and chemicals, enabling the sustainable and scalable synthesis of a wide range of products. By engineering microorganisms to produce desired compounds, synthetic biology is providing an alternative to traditional chemical synthesis methods that often rely on fossil fuels and generate harmful byproducts.
High-value chemicals, e.g. flavours, fragrances and pharmaceuticals, using microbial fermentation. By engineering the metabolic pathways of microorganisms, researchers can create efficient and sustainable production platforms for these compounds. AI tools can aid throughout this process from the design to the extraction stages, by helping design the genetic sequences used or optimising and scaling production systems (García Martín, Mazurenko and Zhao, 2024[12]).
Bioplastics and resins. Researchers have developed synthetic pathways in bacteria and yeast that can convert renewable feedstocks, such as sugars and plant oils, into monomers that can be polymerised into biodegradable plastics. These bio-based plastics have the potential to replace petroleum-derived plastics, reducing our reliance on fossil fuels and developing safe-by-design materials mitigating the environmental impact of plastic waste (Adkins et al., 2012[13]). AI neural networks are being used to predict these bioplastics’ characteristics and find viable replacements to non‑biodegradable products on the market, which could then be produced via microbial synthesis (Kuenneth et al., 2022[14]).
Self-assembling and repairing advanced materials. Scientists have engineered bacteria to produce biofilms that can be used as living materials for applications such as water filtration and bioremediation. These living materials can be programmed to perform specific functions, such as selectively binding to contaminants or degrading pollutants. The unintended release or escape of such living organisms into the environment could, however, also pose a risk for natural microbial communities.
Biofoundries as interdisciplinary spaces
Converging technologies tend to co-emerge with other infrastructure that leverages, enables and drives convergence. In synthetic biology, this process can be seen in the so-called “biofoundry”: an advanced, automated facility designed to accelerate synthetic biology research and biomanufacturing by integrating high-throughput robotics, automation and AI-aided design tools. In the field of synthetic biology, biofoundries, and the institutions, disciplines and skill sets assembled around them, operate as powerful convergence spaces. Catalysing the development of potential products, biofoundries can improve and produce novel knowledge and products. In the field of biotechnology, convergence with AI is just gearing up.
The biofoundry referenced above leverages machine learning and large language models to accelerate the design and production of bio-based products and reduce labour costs via automation. A study at the University of Wisconsin-Madison demonstrated that AI-driven autonomous protein engineering could achieve results three to six times faster than the speed of the (human) researchers at the university (Rapp, Bremer and Romero, 2024[15]). Biofoundries require significant upfront investment, but their long-term operational costs, such as staffing and equipment maintenance, pose a greater challenge to sustainability. In particular, the need for a skilled workforce that can combine knowledge of AI, automation and molecular biology is a serious bottleneck. While technologies and tools are converging, training programmes for researchers and technicians still remain mostly mono-disciplinary.
Policy opportunities and challenges
Some policy issues arising from the convergence of synthetic biology, AI and automation include:
Optimising the regulatory environment. Technology developments can pose challenges for regulatory systems which may become less fit-for-purpose as technologies converge. For AI-enabled synthetic biology, these challenges are exacerbated not only by the speed of technological development but also by the complexity of combinations of AI, synthetic biology, automation, etc. In this convergence, a variety of regulatory regimes apply that encompass R&D in the design of new bioproducts, the use of AI in that context, and the regulation on the safety of the products themselves. This calls for the simultaneous consideration of technological development and regulatory considerations, and supports the need for including ethical, legal and social analysis into the interdisciplinary mix of the convergence space.
Access and freedom to operate. The trade-off between open science and security is key in AI-enabled synthetic biology given the potential biosecurity implications of some applications, as well as open access and commercial application, since fostering trust in the biodata used and the algorithms mobilised is key but so is protecting intellectual property and ensuring economic competitiveness. How can innovators be transparent and build trust while being secure and profitable?
Biodata harmonisation and sharing. AI-enabled synthetic biology relies on the mobilisation of vast amounts of biodata that can then be processed using algorithms. However, the nature of the data infrastructures – both their form and who controls them – is not yet clear. With the potential of industrial monopolies on biodata, and the lack of standards for quality control, interoperability and rules for sharing data, there is a clear challenge for governance. In a similar vein, there is often little transparency on the construction of (bio)databases (what is in the data set and how did they get it?), the means of storing the data (who owns it and how is it managed?) and the quality of the algorithms (what does the outcome from the algorithms actually mean?). This poses a great challenge for governance.
Challenges of oversight with humans out of the loop. Replacing humans with AI, automation and robotics means that increasingly complex and time-consuming processes of synthetic biology development are possible. However, ensuring that human agency has a meaningful place in workflow preserves opportunities for value judgements to be made during the R&D processes as well as quality control, assurance, cybersecurity, and even legal and moral accountability. It is also critical to consider workers’ rights to consultation, participation and protection against unemployment. With the promise of increasingly autonomous design, build, test and learn cycles, what is the best balance between autonomous and human-in-the-loop systems?
Supply chain access and resilience. The COVID-19 pandemic revealed pressures on key supply chains for synthetic biology R&D. With an increased interest in technological sovereignty and rising geopolitical tensions, building resilience in global synthetic biology supply chains is a key challenge going forward. This is particularly key for realising the promise of distributed manufacturing through AI‑enabled synthetic biology, where the need for specific chemical reagents to undertake R&D is key, and is dictated by local resources, demographic pressures and sustainability impact. Beyond the supply risks observed during health crises with border closures, the challenges of synthetic biology on supply chains stem also from the replacement of oil with plant-based or waste-derived sugars as raw materials. Beyond the risks of border closures, it will, therefore, be essential to rethink raw material supply networks to make this industry sustainable.
Cyberbiosecurity as a key issue for research security. The increasing integration of biological research and automation has elevated the significance of cyberbiosecurity – the intersection of cybersecurity and biological sciences. Cyberattacks such as ransomware and distributed denial-of-service attacks on biological research and biomanufacturing facilities pose substantial risks, including economic disruptions (e.g. operational delays, industrial espionage), environmental hazards (e.g. accidental explosions, release of hazardous substances) and public health threats (e.g. unintentional dissemination of infectious agents). However, much of the existing infrastructure supporting biological research and bioproduction was not originally designed with resilience to cyberthreats in mind. This vulnerability is particularly pronounced in under-resourced communities, which frequently rely on secondary markets where older, and potentially less secure, equipment is more prevalent. As a consequence, enhanced oversight and control mechanisms to systematically assess these vulnerabilities and develop effective mitigation strategies might be required (Robinson and Nadal, 2025[16]). Further interdisciplinary research and policy initiatives are essential to strengthen cyberbiosecurity frameworks and ensure the resilience of biological research and biomanufacturing infrastructures against evolving cyberthreats.
Lack of ecological data for applications that are intended for release. With the great pace of innovative and new synthetic biology applications that are intended for release more cases will arise, where a sound risk assessment will be hampered by a lack of ecological data of the receiving environment. A parallel strengthening of biodiversity research will prevent this obstacle and help to make use of these technologies.
Convergence in neurotechnology
Neurotechnology has been defined as “devices and procedures used to access, monitor, investigate, assess, manipulate, and/or emulate the structure and function of the neural systems of natural persons” (OECD, 2019[17]). Neurotechnology is marked by increasing confluence of component technologies, scientific understandings and know-how, particularly AI and BCIs and even new immersive technologies. These technologies and approaches are converging in both the sense of “co-evolution” and “fusion” noted above, and present opportunities for the repair and enhancement of neural functioning (García and Winickoff, 2022[18]). The same confluence also raises new ethical, legal and social implications or exacerbates existing ones (García and Winickoff, 2022[18]).
Assembling institutions, actors and tools around the human brain
The complexity and importance of the human brain – as a terrain of study, engineering, diverse skill sets and disciplines – is opening up forms of innovation that are deeply interdisciplinary, translational and transformational. Indeed, it is actively assembling a broad range of actors, approaches and institutions to the project of generating new therapies, applications and solutions. New institutions devoted to neurotechnology are taking advantage of the convening power of the human brain to generate new kinds of understandings and interventions of neural processes. An excellent example of this is the Wyss Center in Geneva (Box 5.2).
The Wyss Center for Bio and Neuroengineering is an independent not-for-profit translational research centre and “venture builder” dedicated to advancing disruptive innovations in the convergence arena of neurotechnologies. A prototypical convergence space, the centre assembles the skills and knowledge of its personnel, state-of-the-art infrastructure, and business innovation partnerships to generate new solutions for mental and brain health.
Founded in 2014 with the support of Swiss entrepreneur and philanthropist Hansjörg Wyss, the Wyss Center convenes experts from diverse fields such as neuroscience, engineering, software and data analytics, neurosurgery, regulatory and clinical affairs, quality assurance, manufacturing, and business development. Together they pursue challenge-driven research and engineering approaches to the human brain, also engaging in patient-based translation, business activities such as spin-off formation, licensing, joint ventures and asset transactions. The Wyss Center’s diverse portfolio includes brain‑computer interfaces, advancements in neurosurgery, artificial intelligence-driven neuromodulation, breakthroughs in neural imaging, studies on the gut-brain axis, epilepsy management, and clinical applications of optogenetics.
One example of a programme is the USD 23 million “Campus Biotech Lighthouse Partnership for AI‑Guided Neuromodulation”, an inter-institutional collaboration aimed at accelerating translational research and development in the field of neurotechnology and artificial intelligence (AI). The programme seeks to leverage interdisciplinary excellence to explore new implantable neurotechnologies for brain recording, on-chip AI-guided decoding of neural activity into electrical patterns, and precise stimulation of the spinal cord.
Source: Based on information from the Wyss Center (personal communication and website).
BCIs are technological mechanisms enabling direct communication between the brain and external devices. These techniques show potential for cognitive enhancement by influencing neural activity like attention, memory and executive functions without surgery. AI systems are machine-based systems that, for explicit or implicit objectives, infer, from the input they receive, how to generate outputs such as predictions, content, recommendations or decisions that can influence physical or virtual environments (OECD, 2024[19]). Immersive technologies, often referred to as “extended reality”, create environments that blend digital and physical realities to various degrees (OECD, 2025[20]). Key elements of these immersive technologies include: fully immersive technology digital environments that replace the user’s physical surroundings, i.e. virtual reality (Turan and Karabey, 2023[21]); digital overlays on the physical world, enhancing real-world experiences with digital information, i.e. augmented reality (Samuel, 2022[22]); and a combination of the two, with digital overlays that are affected by the physical features like lighting, i.e. mixed reality (OECD, 2025[20]).
The three fields are helping each other accelerate their individual trajectories and integrating to form new applications. AI can enhance immersive technology experiences by providing intelligent responses, personalised content and adaptive environments based on user behaviour and preferences. BCI enables direct communication between the brain and external devices or software, allowing users to control computers or devices using their thoughts. It can lead to more natural and intuitive interactions within immersive technology environments, such as controlling virtual objects or environments through mental commands. In the future, immersive technology might provide a platform for integrating AI and BCI, offering users experiences that respond intelligently to their inputs and cognitive states. But these very same convergences can raise concerns around human autonomy and privacy.
For example, Forsland et al. (2021[23]) describe a BCI system for augmented reality that demonstrates the potential for seamless integration of neural inputs with immersive technology environments. This convergence enables more natural and intuitive interactions within immersive technology environments, such as controlling virtual objects or navigating digital spaces through mental commands. Mental commands can replace or supplement traditional input methods, particularly valuable in situations where physical movement is limited or undesirable (Forsland et al., 2021[23]).
The neuro-AI convergence produces new products and opens research avenues. Some estimates suggest that the global market for BCI will increase to USD 6.2 billion by 2030.2 Implications for healthcare and consumers’ use are profound, therefore requiring ethical reflection and policy consideration. This section illustrates AI-BCI-immersive technology’s key developments, discusses its most critical ethical considerations and identifies salient policy challenges.
Converging medical technologies
More than 3 billion people worldwide (i.e. over 40% of the global population) were living with a neurological condition in 2021 (Steinmetz et al., 2024[24]). Healthcare is undergoing a digital transformation, integrating AI into many aspects of the care, which promises to reduce costs and risks of therapies (Al Kuwaiti et al., 2023[25]). In the area of brain health, AI is creating a paradigm shift in delivery, patient outcomes and medical research. Convergence around neurotechnology can be found in diverse areas of medicine, such as:
Precision medicine: AI algorithms analyse vast amounts of patient data, including genetic information, to tailor treatments to individual patients. When combined with BCI technology, this allows for real-time monitoring and adjustment of therapies based on neural feedback. For example, in the treatment of Parkinson’s disease, AI-powered closed-loop deep brain stimulation systems can adjust stimulation parameters in real time based on neural signals, providing more effective symptom management (Denison and Morrell, 2022[26]).
Robotic surgery: AI-powered surgical robots are becoming increasingly sophisticated, with immersive technology interfaces providing surgeons with enhanced visualisation and control. BCIs are being explored to allow surgeons to control these robots more intuitively, potentially improving surgical precision and reducing fatigue. Recent advancements include the integration of haptic feedback in robotic surgical systems, allowing surgeons to “feel” tissue properties through BCIs, significantly enhancing precision in minimally invasive procedures (Qu et al., 2022[27]).
Neurological rehabilitation: Combining BCI-controlled virtual environments with immersive technology can create highly engaging and effective rehabilitation programmes for patients with motor impairments. Using AI, these systems adapt in real time based on neural feedback, optimising the rehabilitation process (Vourvopoulos et al., 2019[28]). Recent studies have shown that BCI-virtual reality rehabilitation systems induce greater neuroplasticity compared to traditional therapies, leading to improved functional outcomes in stroke patients (Aderinto et al., 2023[29]). A notable example is the use of BCI-controlled virtual reality systems for upper limb rehabilitation in stroke patients, which have shown promising results in improving motor function beyond traditional therapies (Zhang et al., 2020[30]).
Cognitive training: For patients with cognitive impairments, BCI-immersive systems provide personalised cognitive training exercises that adjust difficulty based on real‑time neural activity, potentially enhancing the effectiveness of cognitive rehabilitation. AI algorithms analyse patterns of cognitive performance and neural activity to tailor training programmes that target specific cognitive domains, maximising therapeutic efficacy (Maggio et al., 2023[31]).
Consumer markets
Neurotechnology convergence in the consumer space is significant due to its potential for rapid, widespread adoption and its intimate integration into daily life. Unlike specialised or industrial applications, consumer-focused convergence has the power to reshape social norms, personal habits and even cognitive processes on a massive scale. The consumer market for converging technologies evolves rapidly, with products ranging from electroencephalogram (EEG)-based meditation headsets to advanced augmented reality glasses with neural interfaces. These technologies find applications in gaming, entertainment and education which, together, represent significant economic potential.
The consumer neurotechnology market is rapidly expanding, with a focus on enhancing cognitive performance, emotional regulation and overall well-being. Many companies are pushing towards non-invasive and minimally invasive BCIs for consumer use. These technologies are increasingly moving beyond niche markets and entering mainstream consumer consciousness, driven by advancements in miniaturisation, AI algorithms and user experience design. Other companies are exploring different approaches to consumer BCIs. The patent awarded to Cognixion (Forsland et al., 2021[23]) describes a BCI system for augmented reality that aims to overcome the limitations of wired connections and expand beyond medical lab usage. Their emphasis on offline AI processing and the potential for smart glasses or contact lens integration points to a future where BCIs could become as ubiquitous and unobtrusive as today’s smartphones. Notable examples include Neurable’s brain-sensing headphones, which use EEG to measure focus and provide a personalised audio experience as well as NextMind’s dev kit, which allows users to control digital interfaces using their thoughts.
The integration of EEG technology into everyday wearables, exemplified by Apple’s patent for EEG-capable earbuds (Azemi et al., 2023[32]), represents a significant step towards ubiquitous neurotechnology. This trend towards unobtrusive, consumer-friendly neurotechnology is further exemplified by companies like Sens.AI, whose patent (Telfer, Julihn and Sokhadze, 2023[33]) describes a wearable device for closed-loop transcranial photo biomodulation, which uses light applications to improve processes in the brain and treat mental disorders such as depression. Such technologies blur the line between consumer wellness products and medical devices, potentially offering cognitive enhancement capabilities to the public.
Consumer technologies in the context of the AI-BCI-immersive convergence refer to products and services designed for personal use by the general public, as opposed to medical or industrial applications. These technologies aim to enhance everyday experiences, productivity, entertainment and personal development. Key areas of application include:
Entertainment and gaming: The AI-BCI-immersive convergence in gaming is spawning unprecedentedly immersive technology and responsive entertainment experiences. Forsland et al. describe a BCI system for augmented reality that demonstrates the potential for seamless integration of neural inputs with augmented reality environments (Forsland et al., 2021[23]). The ability of AI to adapt game environments in real time based on a player’s neural and physiological responses could create experiences earmarked for each individual.
Productivity and work: The integration of BCI-controlled interfaces with AI assistants in immersive technology environments promises to revolutionise remote work and collaborative virtual spaces. The Internet of Things may have the potential to bridge physical and digital realities in the “metaverse”, enabling seamless control of work environments (Li et al., 2023[34]). This convergence could enhance productivity and collaboration, allowing for more intuitive and efficient interactions with digital tools and remote colleagues. For instance, the ability to manipulate data visualisations through thought alone, or to instantly access and share information through neural interfaces, could transform the nature of work.
Education and skill acquisition: AI-powered adaptive learning systems, combined with immersive technology environments and BCI inputs, will enable highly personalised and efficient educational experiences. AI systems might be able to analyse a learner’s cognitive states through BCI inputs, adapting the pace, style and content of instruction in real time within immersive technology environments.
Social interaction: AI-mediated social platforms in immersive technology environments, enhanced by BCI inputs, could enable more nuanced and empathetic digital communication.
Personal development and wellness: The AI-BCI-immersive technology convergence enables new approaches to mental health and cognitive enhancement. For example, a closed-loop transcranial photobiomodulation system using cognitive testing demonstrated how AI can be used to optimise non-invasive neuromodulation in real time (Telfer, Julihn and Sokhadze, 2023[33]). These technologies offer the potential for personalised interventions for mental health and cognitive enhancement. AI could analyse patterns in neural activity, behavioural data and environmental factors to provide tailored interventions through BCI.
Policy opportunities and challenges
The convergence of neurotechnology and AI carries promises for human enhancement, provided that policymakers address ethical considerations through adequate governance strategies. Immersive technologies rely on technological enablers (such as machine learning models, data, computational power, etc.) and, if developed and used responsibly, offer significant potential advances. At the same time, integrating neurotechnology and AI raises new ethical questions while exacerbating existing ones. Most urgently, these new research areas and resulting technological products cause risks to individual privacy and mental integrity; informed consent procedures must be updated adequately and ambiguous protection frameworks need to explicitly extend to neural data to avoid unauthorised access to a new kind of data that could reveal personal thoughts and emotions. Such access could then lead to manipulation and control serving marketing or political purposes, depriving individuals of their autonomy and freedom of thought, or giving rise to cyberbullying or harassment. In parallel, like many advanced technological developments, neuro-AI innovation might not be equitably accessible. The high cost and limited availability of products could also make access to innovation highly inequitable, depending on whether health systems cover new applications. Products aimed to enhance cognitive capacities in consumer markets could also heighten equity concerns.
The prospect of cognitive enhancement revives the question of the definition of humanity – its purpose and limits – which can only be tackled through dialogue and deliberation from a broad range of perspectives. Identity, personhood, society and culture are uniquely human concepts whose definitions may vary from one tradition to the next and, hence, deserve careful and pluralistic consideration before picking one technological direction.
In general, policymakers should consider reinforcing agile regulatory oversight mechanisms (with particular attention to the potential for dual use), expand data protection, develop standards across sectors and countries, deploy strategies for broad and fair access, and organise regular opportunities for public engagement. The neuro-AI convergence also foregrounds particular policy needs, including:
Rethinking responsibility: Neuro-AI products raise questions about individual agency and responsibility, especially when AI systems are involved in decision-making processes through neural interfaces. Policymakers must address how to attribute responsibility in such scenarios.
Deploying anticipatory governance strategies: The rapid evolution of neuro-AI convergence requires flexible and adaptive regulatory approaches. To keep pace with technological advancements, regulatory bodies could explore models such as “regulatory sandboxes”, controlled environments where businesses can test products under relaxed regulatory conditions and close supervision by regulators. Iterative review processes that can quickly incorporate new scientific findings and technological developments are also effective.
Adapting funding, insurance and regulatory categories: Neuro-AI products such as AI-enhanced neural implants combine hardware, software and AI components. These hybrid technologies often span multiple regulatory categories, making it difficult to determine appropriate oversight. Policymakers could consider:
Providing a new organisational funding structure to leverage and combine existing programmes, which may lead to the discovery of funding or goal synergies to enhance funding allocation.
Getting insurance companies to support tech companies with reimbursement and patient accessibility. With a strong relationship with insurance agencies, such companies would attract more investment opportunities as the development and rollout of products would stabilise. On the patient side, a more substantial insurance plan would ensure wider access.
Rethinking the medical/consumer dichotomy and instead privileging a risk-based classification or purpose-agnostic approach, for example.
Cross-border data platforms: As AI-enhanced neurotechnologies generate vast amounts of data, policymakers should co-ordinate internationally to create data-sharing platforms and establish governing frameworks. This process will require the consideration of diverse cultural perspectives on data protection, research practices and technological use.
As neuro-AI convergence keeps on advancing, addressing these policy challenges is critical to innovating in a way that protects and advances core values. In this vein, the OECD Recommendation of the Council on Responsible Innovation in Neurotechnology (OECD, 2019[35]), the first international standard in this domain, guides governments and innovators to anticipate and address the ethical, legal and social challenges raised by novel neurotechnologies while promoting innovation in the field.
Convergence in quantum technologies
Quantum science originated in the early and mid-20th century as physicists tried to understand phenomena that classical physics had not been able to explain. The initial breakthroughs – often described as the first quantum revolution – are associated with such scientific luminaries as Niels Bohr, Albert Einstein and Erwin Schrödinger, among many others. These discoveries depicted a quantum world that contrasts sharply with classical physics and everyday experience. It revealed a reality where, among other features: the act of measurement influences outcomes; quantum particles can be correlated or entangled such that the state of one instantly influences the state of another, no matter the distance between them; quantum systems can exist in multiple states simultaneously until measured (“superposition”); and particles pass through objects unhindered (“quantum tunnelling”). The first quantum revolution also gave rise to the creation of a first generation of quantum-based technologies, many of which are central to contemporary science and society, ranging from transistors and semiconductors to lasers, light-emitting diodes and magnetic resonance imagers.
The term “second quantum revolution” refers to the current phase of technological progress that builds on the initial breakthroughs to harness quantum phenomena like superposition, entanglement, and quantum tunnelling for novel and more powerful technologies (OECD, 2025[36]; OECD, Forthcoming[37]).
Three key technologies of the second quantum revolution
The key technologies of the second quantum revolution include quantum computing, quantum sensors and quantum communication devices (OECD, 2025[36]).
Quantum computing holds the promise of advancing high-performance computing in the medium to long term, pushing the boundaries of what is currently considered “computable”. A conventional transistor flips between on and off, representing 1s and 0s. However, a quantum computer uses quantum bits (qubits), which can be in a state of 0, 1 or any probabilistic combination of both 0 and 1 (for instance, 0 with 20% and 1 with 80% probability). Qubits can also interact with other qubits through so-called quantum entanglement, enabling parallel processing. Algorithms designed to run on quantum computers can, in principle, excel at specific problems like factoring large numbers (Shor’s algorithm), database searching (Grover’s algorithm) and simulating systems where quantum effects are important.
Quantum sensing has the potential to significantly advance measurement capabilities, enabling sensitivity and precision on a par with the smallest perturbations found in nature (Degen, Reinhard and Cappellaro, 2017[38]). As their performance improves, these sensors could enable the measurement of phenomena such as time, gravity, magnetism, temperature and electromagnetic spectrum analysis at scales and levels of accuracy unattainable with classical methods (Ezratty, 2023[39]). Applications range from better medical imaging (as next‑generation atomic clocks synchronise imaging processes); easier mapping of the ocean bed and detection of subsoil features on land (thanks to gravimeters) as also seen in the next section on satellite earth observations; and new means of navigation (using ultra-accurate measurements of the earth’s magnetic field).
Quantum communication is an emerging technology that uses the properties of quantum systems to enable transmission and manipulation of information through quantum networks. The best-known application of quantum communication is quantum key distribution (QKD), which uses quantum states (typically photons) to enable two parties to generate a shared, secret random key. The source of the security is physical law, because measuring a quantum state alters it irrevocably. The quantum-secured keys cannot be intercepted without detection. This is different from classical encryption, which can potentially be broken with enough computing power (Wikipedia, 2025[40]).
These three technologies are emerging. Among the three, quantum computing is the least advanced and quantum sensing the most advanced. Significant technical and research challenges still need to be solved. However, achieving technically and commercially viable systems could disrupt many sectors of economic and social life. The following sections describe three areas of science and technology that have converged, or are in the process of converging, with quantum technologies: AI, biology and engineering.
Quantum technologies converging with artificial intelligence
AI is being used in every domain of science and across all stages of the scientific process (OECD, 2023[41]). Quantum science is benefiting as much as any other branch of research, from automated scientific literature review to machine-assisted design of experiments.
AI is also contributing to quantum science and technology in a variety of specific ways. For example, machine learning techniques are being employed to decode and correct errors in qubits (Usman, 2024[42]). Reinforcement learning can help to design optimal control of qubit operations (Wolf, 2024[43]). In addition, given that every quantum device is slightly different, reinforcement learning can analyse a machine and its patterns to help fit algorithms specifically to that device (Padavic-Callaghan, 2024[44]; Vicentini, 2024[45]). In addition, AI is likely to support quantum sensing, distinguishing noise from feint sensor signals, and helping to understand sensor data.
Much attention has also been paid to the possibility that quantum computers could enhance AI systems. To date, this possibility is largely theoretical. Hybrid quantum-AI algorithms have been tested on small problems, and companies are exploring quantum neural networks for tasks like natural language processing (Quantinuum, 2025[46]). A key research topic focuses on using quantum computers to reduce the complexity and cost of using AI models. This might be achieved by having quantum computers describe complex features of a system of interest, such as a chemical reaction, more simply (i.e. with fewer parameters) than classical systems, before applying AI (Brooks, 2023[47]).
Vicentini (2024[45]) reports a recent lowering of expectations among researchers regarding quantum AI. He holds that quantum computers may not greatly advance AI because they struggle to process large-volume data from neural networks. To date, it has only been possible to maintain coherence across qubits for tiny fractions of a second, meaning that only very short calculations are possible. He considers that quantum computers will have ongoing problems in executing AI algorithms on large data sets because of such short coherence times. Trying to increase the rate at which data are input and output is written will introduce more calculation errors. However, he and others are optimistic that quantum computers will be very useful for applications that require limited input and output data, but much processing power.
Progress in harnessing quantum effects for AI faces several challenges. New algorithms are required. Quantum computers output probabilistic results – an answer to the same problem may differ every time a machine computes – not directly compatible with classical data pipelines, and quantum sensors produce novel data types requiring new AI processing techniques. Interdisciplinary expertise is also scarce. Computer scientists often know little about, or struggle to keep up with, theoretical developments in quantum computing. Effective integration of quantum and AI technologies demands close collaboration between quantum physicists and AI researchers.
Quantum technologies converging with biology
Recent years have seen rapid growth in a field of science known as “quantum biology”. Quantum biology studies the convergence of quantum physics principles with biological systems, exploring how life’s mechanisms may function at the quantum scale, and how natural selection has found quantum-based solutions suited for different ecological niches (Al-Khalili and McFadden, 2014[48]).
Quantum biology has its origins in a lecture given by Niels Bohr in 1932, entitled “Light and Life”, where, among other things, he discussed the atomic-level sensitivity of retinal cells (Bohr, 1933[49]). More recently, science has discovered evidence that quantum mechanical mechanisms likely underpin processes and functions such as photosynthesis, navigation in birds and the sense of smell (Al-Khalili and McFadden, 2014[48]).
In addition to helping explain the natural world, quantum biology is yielding technologically useful knowledge. For example, the speed with which plants convert sunlight into carbohydrates – one million billionths of a second – minimises energy loss in the form of heat. How plants achieve this remained a mystery until 2007 when biophysicists showed that plants use a form of quantum computation (Biello, 2007[50]). Scientists reasoned that the mechanisms plants employ to achieve near-perfect efficiency in harvesting energy might be mimicked in artificial systems for energy generation and capture. Indeed, experimental chemists have used this knowledge to build plant-like light-harvesting arrays (Romero, Novoderezhkin and van Grondelle, 2017[51]). Another area where quantum biology may yield technologically useful insights is in quantum biosensing (Box 5.3).
Scientists are exploring quantum sensors built from biological materials. For example, researchers recently engineered a fluorescent protein from a luminescent jellyfish. This glowing protein can be produced inside living cells and detect tiny changes in its environment with much greater sensitivity than standard sensors (Wilkins, 2025[52]). Potential applications include tracking biochemical signals or early disease markers from inside the cell.
In another laboratory experiment, a natural protein found in robins’ eyes was shown to function as a magnetoreceptor, sensitive to Earth-strength magnetic fields. This suggests that such birds might use what is essentially a biological quantum sensor to aid navigation (Offord, 2021[53]). Indeed, recent research shows that biological magnetic sensing operates at near the limit of what is physically possible in terms of sensor volume, measurement time and measurement certainty (Wright, 2025[54]). While there are not yet any commercial biomimetic quantum devices, such discoveries are guiding research on the design of lab-made quantum magnetometers, potentially enabling ultra-sensitive compasses or biology‑based medical imaging.
Sources: Wilkins (2025[52]); Offord (2021[53]); Wright (2025[54]).
Protein-based quantum systems
Quantum phenomena have long been observed in proteins (such as enzyme reactions involving tunnelling and light-absorbing proteins showing coherence). However, using proteins as quantum devices is a recent development. In the past decade, researchers began exploring protein-based quantum systems, where the proteins themselves serve as carriers of quantum information. Research on protein-based computing is exploratory, and practical applications are still far off. However, a few key advances demonstrate the concept’s potential. For example, researchers at the University of Peking recently showed that DNA could act as storage and computing elements in quantum devices (SciTech, 2025[55]).
Convergence between quantum research and engineering
Beyond quantum technologies proper, progress in the quantum revolution relies on progress in several enabling fields of technology. Many of these have uses outside the quantum realm. Their progress relies in large part on the ingenuity of chemical, electrical and mechanical engineers (for an example, see Box 5.3). Two lesser-known examples are vacuum tubes and cables.
The Argonne National Laboratory near Chicago (Illinois) in the United States is a pre-eminent and interdisciplinary science and engineering research centre founded in 1946. The Argonne Quantum Institute combines expertise in quantum computing, sensing, photonics, communications, materials science and high-performance computing. Argonne enjoys an integrated interdisciplinary ecosystem, including national facilities like the Advanced Photon Source and the Center for Nanoscale Materials.
In June 2025, Argonne celebrated multiple quantum milestones in computing, communication, sensing and materials improvements. These included creating and characterising qubit materials, harnessing supercomputing to advance quantum computing, building quantum networks over a range of distances, developing sensors for science, strengthening the supply chain of materials for quantum devices and systems, and supporting the quantum ecosystem through partnerships (Argonne National Laboratory, 2025[56]).
Argonne also leads the Department of Energy’s Q-NEXT Center, established in 2020. Q-NEXT brings together leading experts from the national laboratories, universities and technology companies to solve cutting-edge challenges in quantum information science. Q-NEXT’s industry partnerships accelerate translation from lab to marketplace. The Argonne National Laboratory is also a founding partner of Duality, the first programme in the United States dedicated to accelerating start-up companies focused on quantum science and technology (Argonne National Laboratory, 2021[57]).
Several factors help to explain the Argonne Institute’s success. One is national-level co-ordination and funding. The Department of Energy has supported five National Quantum Information Science Research Centers across the United States, including Q-NEXT, supporting fundamental research and applied translation in complementary ways. The Department of Energy’s Office of Science recently announced the availability of USD 625 million to support the centres (Trueman, 2025[58]). Another reason for the institute’s success is access to world-class user facilities: photon sources, nanoscale materials centres, multi-purpose quantum foundries and high-performance computing environments, among others, all enable cutting-edge quantum experiments. Argonne is also characterised by well‑structured industry-academic-lab pipelines: it drives collaborative ecosystems across universities, laboratories and the private sector, nurturing spin-outs, training researchers and helping bring prototypes to commercial readiness.
Sources: Argonne National Laboratory (2021[57]; 2025[56]); Trueman (2025[58]).
Vacuum components: To reduce disturbance of qubits, vacuum technologies are key to some forms of quantum computing. Recent theoretical work suggests that vacuum tubes, if designed and arranged properly, might also be able to carry photons – containing quantum data – for thousands of kilometres without attenuation (Williams, 2024[59]).
Cabling: Cabling plays a key role, particularly with solid-state qubits. The cables used need to carry delicate quantum information between different parts of a quantum computer or between nodes in a quantum network, all while shielding the quantum information from disturbances from external sources such as heat, electromagnetic radiation, vibrations and extreme cold. Superconducting cables are expensive at around EUR 3 000 per metre and come from a single vendor from Japan (Ezratty, 2023[39]).
Bringing the cold of deep space to computing
One of the most important technologies in the quantum realm is cryogenics. Cryogenics is a branch of physics that studies the behaviour of materials at extremely low temperatures, typically below ‑150°C (‑238°F). Cryogenics is critical to progress across multiple industries and domains of science, including space exploration, medicine and energy technologies. For instance, the James Webb Space Telescope uses cryogenic cooling to detect weak infrared signals from space. In medicine, cryogenics helps to preserve cells, tissues, embryos and organs. And hydrogen fuel cells and storage systems rely on cryogenic hydrogen (Connor, 2010[60]).
Cryogenics draws from several scientific disciplines, particularly physics and materials science. Engineering disciplines – particularly mechanical, electrical and chemical engineering – are also essential for designing practical cryogenic system. Cryogenics is critical to the operation of superconducting quantum computing, an architecture used by companies like IBM and Google (Pakin and Coles, 2019[61]). At ultra-low temperatures, electrons can flow in metal circuits with zero resistance, enabling the precise quantum states needed for computation. The extreme cold also helps isolate the quantum system from its environment, extending the time during which qubits maintain their quantum states, allowing the execution of more complex quantum algorithms.
Achieving these ultra-low temperatures presents significant engineering challenges (Gainey, 2019[62]). An additional challenge is to integrate cryogenic environments with electronic control systems, which necessitates materials and designs that can operate reliably under such extreme conditions.
While cryogenics is a mature field of science and technology, several areas of progress are needed, and research on these is active. For instance, more energy-efficient systems are required, as current methods for achieving ultra-low temperatures requires significant energy. Breakthroughs in miniaturising cooling systems will also make cryogenic technology more practical for quantum computing applications while enhancing efficiency and reliability.
Policy opportunities and challenges
The preceding section underscored the close relationship between research, engineering and experimentation. Institutions that can enhance such interactions are likely to be particularly effective in driving progress in quantum science and technology. Indeed, at least one recently announced private sector breakthrough in quantum chip development has been attributed, in part, to the large tech company in question having brought the manufacturing process in-house, thereby facilitating the needed interactions (Waters, 2024[63]).
Several research institutions and large companies have sought to facilitate close iteration between theoreticians, applied researchers and research engineers. An example is the Princeton Quantum Initiative, an interdisciplinary programme at Princeton University, described as “providing an integrated research environment at Princeton where experimentalists, engineers, and theorists work closely together.” The Princeton website continues “This interdisciplinary collaboration accelerates development of next-generation quantum computing and quantum sensing technologies by linking theory, materials engineering, and device measurements in a single loop” (Princeton Quantum Initiative, n.d.[64]).
In the private sector, one of the world’s largest quantum computing companies, Quantinuum, was formed in late 2021 from the merger of a quantum software and operating systems company, Cambridge Quantum, and Honeywell Quantum Solutions, a developer of quantum hardware. The merger integrated more than 370 scientists and engineers into the same organisation. In an example of the sort of convergence space referred to at the start of this chapter, the announcement of Quantinuum’s creation emphasised the value of bringing together a unified team of hardware engineers, software experts and scientists.
Policy can help to increase opportunities for the sorts of exchanges described above. For example, both Japan and the United Kingdom have organised part of their national quantum strategies around funding quantum innovation hubs specifically intended to facilitate collaboration between academia and the private sector. This is not exactly the same as bringing all relevant competencies under the same roof, but fostering nuclei of institutions housing mixed-discipline expertise is a step in the right direction.
As referred to throughout this chapter, policies that support inter-disciplinary education are essential. Demand is growing for professionals who have some proficiency in quantum science and technology, but not necessarily specialists, as well as for science, technology, engineering and mathematics graduates with complementary skills suited to the quantum industry (White House Office of Science and Technology Policy, 2022[65]). The interdisciplinary nature of quantum technologies – encompassing fields such as mechanical engineering, optical engineering, systems engineering and application development – has underscored the need for academic institutions to offer master’s programmes that align with industry requirements. In addition, there is scope for universities to develop shorter postgraduate certificates or continuing education programmes with quantum curricula. These could help to meet the growing demand for skills upgrading and diversification among adult learners (Goorney et al., 2024[66]).
Convergence in space-based earth observation
Space-based observation is the collection of information about the earth’s surface, atmosphere and ocean from satellites equipped with sensors that detect reflected or emitted energy across various parts of the electromagnetic spectrum. Earth observation products provide intelligence, supporting decision making in many sectors, and economic, security and environmental policies. Data are collected by both public and commercial satellites, with US and European government programmes providing often open data with national and global coverage while commercial providers, working closely with government agencies, focus on more specialised imagery with higher resolution or revisit times.3
The increasing need to provide timely, accurate and actionable insights for policy, security and economic decision making is driving the convergence of different EO technologies. Convergence can be seen in the integration of multiple systems and disciplines. New EO systems no longer rely solely on satellite engineering progress but increasingly combine advances in optics, lasers, cloud and edge computing, AI, quantum technologies, robotics, and in situ sensor networks. This integration enables near real-time data collection, processing and dissemination while supporting applications like weather monitoring, disaster response and strategic intelligence (OECD, 2023[67]). As these domains co-evolve, EO becomes part of a broader, interconnected technological ecosystem where innovation in one area – such as AI-driven analytics – directly accelerates capabilities across others.
Interdisciplinary innovation centres and data platforms as convergence spaces
Interdisciplinary innovation spaces and advanced data platforms are now central to the technological convergence driving modern EO (OECD, 2020[68]). Innovation hubs in the space community, such as the European Space Agency’s Φ-lab and the United States’ National Aeronautics and Space Administration’s (NASA) Jet Propulsion Laboratory, foster collaboration between EO specialists, AI researchers, optics, robotics and quantum experts from industry, generating new space systems and applications that could not emerge in isolation.
This convergence is strengthened by collaborative platforms focused on data analytics and sharing, such as the European Union’s Destination Earth, NASA’s Earth System Observatory, the United States’ National Oceanic and Atmospheric Administration’s Open Data Dissemination programme, Digital Earth in Australia, satellittdata.no in Norway, Satellite Data Portal in the Netherlands, and Europe’s Copernicus Data Space Ecosystem (T Systems, 2024[69]). Acting as operational interfaces, these initiatives increasingly rely on cloud-based platforms and digital twins to merge satellite imagery with open data from in situ sensors, Internet-of-Things networks and meteorological models. Supported by high‑performance computing and AI, they can transform massive and complex data sets into timely, actionable insights that are then used and transformed further by public and private stakeholders (e.g. Google Maps).
These interdisciplinary innovation spaces and advanced public data platforms are becoming not only technical enablers but also strategic assets for convergence, supporting data sharing while also accelerating digital innovation across sectors that depend on reliable earth intelligence.
Convergence with optical systems and laser technologies
Optical systems for space-based EO largely originate from advances in fields such as astronomy, defence and precision manufacturing, including in medical fields. Technologies initially developed for telescopes, deep-space imaging and military reconnaissance have been adapted to create lightweight, high-resolution and multispectral satellite instruments. Laser systems, including light detection and ranging (LiDAR) and laser altimeters, similarly draw on progress in photonics, quantum optics and high-stability laser sources from scientific research and industrial applications.
However, this convergence presents several challenges. Optical and laser instruments require extreme precision and stability to function in the harsh conditions of space, demanding advanced thermal control, vibration mitigation and radiation-hardened components. Integrating cutting-edge optics with satellite platforms also raises issues of size, weight and power constraints, which can limit the deployment of the most advanced sensors on smaller satellites. Moreover, the rapid pace of innovation in optical and laser technologies outpaces traditional satellite development cycles, making it difficult to continuously leverage the latest advances without redesigning mission architectures. Despite these challenges, the convergence of EO with optics and lasers continues to drive transformative improvements in spatial resolution, accuracy and the range of measurable environmental variables.
Convergence with artificial intelligence
Space-based EO technologies are also converging with advances in AI. The scope and impact have evolved significantly over time, moving from experimental applications to mainstream operational use, from inputs to satellite engineering and manufacturing (including very small satellites and sensors), and improved data processing.
In the late 1990s and 2000s, the revolution of small satellites was accompanied by the rise of AI in EO, through new space subsystems manufacturing processes, machine learning techniques like neural networks, and support vector machines for improved land cover classification and cloud detection in EO applications.
In the 2010s, the surge in high-frequency data from satellites like the European Sentinel and American Landsat programmes, combined with cloud computing, enabled operational AI applications for large-scale environmental monitoring and change detection. For example, the Global Agriculture Monitoring initiative by the Group on Earth Observations combined EO data, weather information and AI-trained models to predict where, when and what crops were growing worldwide, in support of market transparency and early warning of production shortfalls (GEO, 2024[70]).
Since the early 2020s, the combination of satellite sensors based on new optics, deep learning and onboard AI have driven a new era of autonomous EO, allowing satellites to pre-process, prioritise and react to observations in real time in orbit, supporting disaster response and multi-domain data fusion. As an example, the European Space Agency satellite Φ-sat-1 uses AI as part of its onboard processing to discard cloudy images, reducing downlink needs (ESA, 2024[71]).
The emergence of foundation models based on EO data may represent a turning point for further use of satellite imagery data, as they lower barriers of access such as advanced technical expertise and access to training data sets while strengthening analytical capability. A NASA and IBM-led partnership had by 2023-2024 created the Prithvi models for EO and weather and climate (Hugging Face, 2024[72]). The NASA/IBM Prithvi-EO 2.0 model is pretrained on some 4.2 million data points from the global harmonised Landsat and Sentinel-2 data set4 and propose applications for carbon flux estimation, landslide detection, burn intensity estimation, crop pattern identification, flood mapping, etc. An important functionality is the Multi‑Temporal Cloud Gap Imputation, which fills gaps in satellite imagery caused by cloud cover, a regular problem with satellite observations (NASA, 2024[73]; IBM, 2024[74]).
Convergence with quantum technologies
As seen in previous sections, one key application of quantum technologies is improved remote sensing. Future advances in space-based EO are increasingly tied to convergence with quantum technologies, which promise breakthroughs in sensing, communications and navigation.
Quantum gravimeters and magnetometers could allow satellites to detect minute changes in the Earth’s gravity and magnetic fields, enabling more precise monitoring of groundwater, ice mass loss and subsurface structures. One technology being studied for these different types of gravity field measurement is cold atom interferometry, which has been tested in the NASA‑funded Cold Atom Lab on the International Space Station. The Horizon Europe research programme is also funding technology development in this area. Quantum clocks and communication systems offer as well ultra-precise timing and secure data transmission, enhancing the reliability of EO networks and global positioning integration. NASA established the Quantum Artificial Intelligence Laboratory in 2012 to advance the development of quantum computing hardware and to learn where and how the application of quantum computing could be beneficial (NASA, 2024[75]).
However, this convergence faces significant challenges: quantum sensors are highly sensitive to environmental disturbances such as temperature fluctuations and radiation, making space qualification complex; their miniaturisation for satellite deployment is still a work in progress; and integrating these cutting-edge instruments into operational EO missions requires rethinking satellite architectures and data-processing pipelines. Despite these hurdles, the fusion of quantum technologies with EO holds the potential to transform global monitoring capabilities for earth and ocean science, security, and resource management.
Policy opportunities and challenges
The use of satellite imagery is associated with productivity gains and improved product quality in the public and private sectors (OECD, 2023[67]; 2024[76]). Convergence with optics and laser technologies, AI, quantum, and other technologies via interdisciplinary innovation centres and data platforms as convergence spaces are enabling OECD governments to diffuse public satellite imagery to foster innovation and economic benefits.
However, the convergence of these technologies, which is amplifying the value and global reach of space-based EO, carries significant policy implications that will require careful attention from policymakers:
Magnified security challenges: Increased availability of higher resolution data magnifies security challenges linked to the malicious exploitation of information on military movements, physical infrastructures, forest fires, etc. A small number of OECD countries have explicit EO data regulations in place (Canada, France, Germany, Japan and the United States) (Harris and Baumann, 2021[77]). These frameworks regulate the conditions for reporting and/or disseminating private sector data for national security purposes, typically addressing technical characteristics such as temporal, spatial and spectral resolution, frequency domains, etc. In Japan, for instance, there are licensing thresholds linked to “distinguishing accuracy of target”, such as vehicles and ships – for optical sensors this accuracy should not exceed 2 metres. In 2020, the United States introduced a new tiered licensing approach for private EO systems, linking stringency to the existence and technological capabilities of foreign competition (Harris and Baumann, 2021[77])
Ethical use of satellite imagery: There is also growing reflection on the ethical use of EO data related to data collection, sharing and ownership. The main issue is not necessarily individual data privacy (other technologies are generally less expensive), but challenges linked to broader scale physical phenomena. For example, asymmetric access to information on physical environmental characteristics (e.g. water levels) could create unfair economic advantages in land transactions. (NSpC UAG Climate and Societal Benefits Subcommittee, 2023[78]).
AI and trust: Uptake of earth observation data beyond government agencies has so far proven difficult for multiple reasons, including, for instance, high investment costs (OECD, 2024[76]), the need to process and calibrate EO data against other data sets (UNECE, 2019[79]), and lacking or poor quality reference data sets (e.g. economic surveys in low-income countries) for satellite-based model validation (Burke et al., 2021[80]). As a result, potential users do not trust the technology because they lack the means, know-how or reference data to properly test predictions. The introduction of AI models could further deepen distrust in these technologies, in particular because there seems to be widespread use in the EO field of AI methods that require random iterative searches and that are not fully repeatable (Pesaresi et al., 2024[81]; Gevaert, 2022[82]). Efforts to use interpretable models, such as those employed by the EU Joint Research Centre to develop the Global Human Settlement Layer, are, therefore, particularly important.
Societal transformations will require harnessing the dynamism of technological convergence, a trend emerging with particular force in the context of AI and digitalisation, synthetic biology, quantum technology, and space-based earth observation. This chapter has laid out a definition and conceptual model of convergence: it can describe the confluence of technologies but also be a process. Convergence can be enhanced by fostering “convergence spaces”, i.e. physical, digital and technological infrastructures and platforms that promote the integration of tools, fields and skilled workers. If designed correctly, these can help optimise tangible and intangible value, foment collaboration, and promote technological integration, for example to enhance sustainability. The creation of convergence applications results from various cross‑disciplinary and cross-sectoral integration efforts throughout the development process, resulting in new applications, industries, and fields of research and development.
The assemblage of interdisciplinary assets sometimes requires forces and incentives to stay together, whether they are financial, institutional or governance-related. This chapter focused on the ways that common technical challenges can also help create convergence spaces, in particular in four areas: the genome, the brain, the atom and space. Each of these terrains – and their associated challenges for their understanding, engagement and design – are being perceived as requiring interdisciplinary assets to open them up to view, to intervention and to exploitation. These four areas help carve out the convergence spaces and trading zones where parties can contract around access to resources, current discoveries and downstream inventions. Ultimately outputs like new knowledge, approaches and partnerships can feed back into the platforms, enhancing their value.
These four key areas of technology exhibit different facets, challenges and opportunities of convergence:
In the field of synthetic biology, AI-powered protein design can create molecules with novel properties and reduce research time and costs with the potential to enable personalised therapies while at the same time posing new challenges. For example, the very same efficiency achievable through convergence has made the potential nefarious misuse more concerning, i.e. the potential engineering of viruses.
In neurotechnology, convergence with immersive technologies and AI present opportunities for, for example, the treatment of mental illness and the enhancement of well-being, but the new powers to mine large data sets are raising critical questions of safety, societal trust, privacy, equity and discrimination.
In quantum technologies, research is expanding on potentially valuable synergies between quantum science and technology and AI, and even biology, among other fields. Engineering innovations relevant to many sectors, such as vacuum components and cabling, are helping to drive progress in quantum technologies, but as yet the tangible impact of these interactions, in terms of market-ready technologies, has still to be felt.
In space-based earth observation, the convergence of AI and digital technologies and satellite imagery technologies has led to multiple new applications ranging from food security monitoring to methane emissions alerts. But it also creates challenges that need to be addressed, such as potential malicious use and risks to national security, trust and asymmetric access to information.
The case studies make clear that AI is a critical – but not the only – driver of convergence today, both in terms of products (the additive factor of AI to existing and emerging technologies) and processes (the engagement of technological development itself). Hand in hand with the digitalisation of science, technology, innovation and society has come the influence of AI in these domains. AI promises to leverage data-rich innovation environments with new and powerful capabilities to learn, optimise and generate new content and processes. Many commentators draw a direct line between the rise of AI in science, technology and innovation and processes of convergence, noting how AI has brought together heretofore unrelated technological domains in ways that promote faster and deeper convergence (Ma and Wu, 2024[1]). This process can be seen in industrial processes as much as in science, although the diffusion of AI in industry is patchy and concentrated. Furthermore, convergence between AI and other fields of technology is likely to accelerate as AI is developed that can work on smaller data sets, opening it up to more potential niche applications. While it remains a frontier of research to better understand AI as a driver of technology convergence, the logic and early experience support this conclusion.
At the same time, the convergence of technologies is giving rise to unique policy dimensions and governance challenges that must be addressed should technologies achieve their full potential. In synthetic biology and AI convergence, for example, different regulatory regimes have different requirements. Synthetic biology is relatively heavily regulated as a legacy with a long history, whereas AI is much less regulated with compulsory legal provisions but codes of practice and self-regulation playing a much bigger part of its governance approach in many OECD countries. As a consequence, entrepreneurship in the AI-biology space may face more pronounced regulations since AI-synthetic biology products are subject to multiple regulatory regimes. Indeed, the hybrid nature of converging technologies raises specific ethical questions and policy challenges insofar as they may fit uneasily into traditional ethical and legal categories such as medical vs. consumer use and therapy vs. enhancement. In these cases, adapting governance to facilitate research use, clinical applications and market diffusion is needed while mitigating associated new risks to privacy, safety and autonomy.
While many governance approaches emphasise the need to mitigate risks, intentional and unintentional harms, and safety and consumer protection, it is also important to account for potential benefits in risk analysis and technology appraisal. In times of polycrises, directing technologies towards areas with the maximum positive impact is desirable. How can this be hardwired the best into agile and anticipatory governance frameworks? One approach can be found in the Framework for Anticipatory Governance of Emerging Technologies (OECD, 2024[19]), by shaping agenda-setting, helping draw “red lines” and influencing deployment practices – but it requires identifying a set of starting values to be deliberated in inclusive multistakeholder fora.
What can governments do?
In the face of these trends and future prospects around the general phenomenon of technology convergence, governments could take a number of steps to help maximise the positive impacts of convergence while minimising potential risks. These include:
Invest in deeper forms of interdisciplinary research. Support approaches that synthesise diverse knowledge, technological methods and approaches, and academic cultures from, inter alia, life sciences, ecology, physics, humanities, computational sciences, mathematics, engineering disciplines and technology assessment research.
Build convergence spaces with technological and collaborative platforms. Leverage different funding models, access rules and technology transfer structures to shape the technological and collaborative platforms that are conducive to convergent technologies. Invest in shared databases and other infrastructure that can leverage AI and other enabling technologies. There is no one-size-fits all approach, but institutional policies can shape the convergence space to optimise for innovation.
Deploy anticipatory governance. Use the OECD Framework for Anticipatory Governance of Emerging Technologies, launched at the 2024 OECD Science and Technology Policy Ministerial meeting. Tools include:
Strategic intelligence. Converging technology policies should foster shared forms of strategic intelligence, involving the comprehensive analysis of technology’s potential directions, economic stakes and societal implications. Recognising the unpredictable nature of converging technologies, robust tools like horizon scanning, foresight and technology assessment should be employed to anticipate future challenges, inform governance strategies and aid strategy formation (see Chapter 7).
Agile regulation. To leverage the vast potential of convergence, policymakers should develop adaptive systems that can keep pace with rapid technological change. These adaptive systems should embed policy experimentation that makes greater use of, for example, policy innovation labs and regulatory sandboxes (see Chapter 7).
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← 1. The Convention on Biology Diversity’s new ad hoc technical expert group developed an operational definition of synthetic biology, which describes it as “a further development and new dimension of modern biotechnology that combines science, technology and engineering to facilitate and accelerate the understanding, design, redesign, manufacture and/or modification of genetic materials, living organisms and biological systems”.
← 2. See https://www.weforum.org/stories/2024/06/the-brain-computer-interface-market-is-growing-but-what-are-the-risks.
← 3. Revisit time refers to the time elapsed between observations of the same ground point.
← 4. Free and open data sets from the US and European Landsat and Copernicus programmes are the backbone of digitally enabled data analysis. They provide de facto standards of geometric (time and location), spectral (colour) and radiometric (colour intensity) calibration, allowing an accurate detection of change (NGAC, 2020[83]). These data sets have furthermore spurred the current wave of AI-fueled innovation in applications and foundation models.