New Foresight Centre Study Identifies the Most Destructive Global Trends for Humankind
A team of researchers from the HSE International Research and Educational Foresight Centre has examined how global trends affect the quality of human life—from life expectancy to professional fulfilment. The findings of the study titled ‘Human Capital Transformation under the Influence of Global Trends’ were published in Foresight.
Unlike most recent studies, which focus on the impact of one or several isolated trends on human development, the HSE Foresight Centre team, led by Anna Grebenyuk and Yulia Milshina, analysed 48 global trends classified into six categories: health; education and labour; society and values; economy; policy and regulation; ecology and the environment. Around 400 international and Russian experts from various fields took part in a Delphi survey, assessing both the strength and the direction of each trend’s impact in order to determine whether it contributes to or hinders specific components of human capital.
Such a broad scope enabled the researchers to develop an index measuring the impact of trends on different aspects of human life, including both general skills required for successful integration into society and profession-specific competencies.
Only six trends received an unequivocally positive assessment across all 12 components of human capital: the transition to lifelong learning (+12), the growth of digital literacy (+12), the development of the creative economy (+12), the transition to sustainable development (+12), the introduction of intelligent technologies in city/house infrastructure management (+12), and online education and digital technologies in education (+10).
The most destructive trends identified by experts include the spread of unhealthy lifestyles (–12), the expansion of digital control (–11), and rising inequality (–10). Trends such as the polarisation of society and the fragmentation of the internet and technological ecosystems also fell into the ‘red zone,’ with scores of –11 and –5 respectively.
The study found that most trends cannot be assessed unambiguously in terms of their benefits or harms for individuals. For instance, the socially charged trend towards labour automation and robotisation appears broadly neutral (+2) for human capital. The researchers explain this by noting that the positive effects of introducing robots—such as improved quality of life and enhanced safety—are offset by negative consequences, including rising unemployment, violations of human rights, and declining levels of social trust.
The strongest interdependence between trends and human capital was observed in the following aspects of human life:
Level of education and competencies (the demand for professional skills is growing faster than education systems can meet it)
Living conditions (comfort, safety, and infrastructure quality are highly dependent on political and economic factors)
Opportunities for professional fulfilment (labour market flexibility and access to retraining depend more on socio-cultural and psychological factors than on economic ones)
Unemployment, inequality, and environmental pressure are the least affected by the aggregate impact of global trends. This does not imply that they are resistant to global challenges; rather, it highlights their direct dependence on political and institutional decisions at the national level.
The authors of the study emphasise that the proposed approach is not merely an academic exercise but a tool for strategic planning. Since trends rarely operate in isolation, policy decisions must take these bundles of effects into account in order to amplify positive outcomes and minimise negative consequences.
For example, the model suggests that reducing unemployment requires investment in lifelong learning and new forms of employment, rather than attempts to counteract automation, as it is social trends that exert the strongest positive influence on the labour market.
At the same time, increasing life expectancy depends primarily on the transition to preventive and personalised medicine, the integration of digital technologies into healthcare, and the development of participatory (collaborative) medicine, rather than on economic incentives alone.
The proposed methodology allows impact indices to be recalculated as new data emerges, making it a flexible tool for long-term forecasting. The same trend may manifest differently in developed and developing countries. Moreover, trends themselves evolve, and certain technologies—such as artificial intelligence or neurointerfaces—may fundamentally transform the very subject of assessment over time.
An open database of global trends in human capital development is available on the website of the HSE Human Capital Multidisciplinary Research Centre (in Russian).
The article can be accessed here.
See also:
How to Assess Students’ Knowledge in the Age of AI
A researcher at HSE University has proposed a flowchart to help lecturers decide how to assess students who use artificial intelligence. It shows where the use of AI should be restricted and where it can be incorporated into the learning process. The article has been published in IT Professional.
Scientists Train Neural Network to Generate Process Plans from 3D Models
Researchers at the HSE FCS AI and Digital Science Institute have developed CAD2TechSpec, a framework that converts 3D models of mechanical parts into machining process plans—step-by-step instructions for machine tools. The solution aims to reduce the time required for the design and preparation of technical process documentation in mechanical engineering, aircraft manufacturing, and other high-tech industries. The study findings have been published in PeerJ Computer Science.
Biologists Discover 'Molecular Fingerprint' of Preeclampsia
Researchers at HSE University employed a new method to model hypoxia in placental cells during pregnancies complicated by preeclampsia and identified molecular markers of tissue hypoxia. Since hypoxia is one of the key mechanisms underlying preeclampsia, these findings are important for a more accurate and timely diagnosis of the disease and for the development of effective treatment methods. The paper has been published in Placenta.
‘Hedgehog’ Versus ‘Relatives’: Researchers Measure How the Brain Responds to Unexpected Words During Natural Speech
Russian neurophysiologists, including researchers from HSE University, have demonstrated the feasibility of using event-related fields (ERFs) to study brain activity during natural speech perception. The researchers showed that this approach can be applied not only to individual words but also to continuous speech. Their findings indicate that words whose meanings differ significantly from the preceding context require longer processing times. The study also reveals that the brain processes function words in two stages: first, it identifies their grammatical role and then uses this information to predict the next word. The study has been published in Frontiers in Human Neuroscience.
HSE Researchers Create New Corpus of Early Child Speech in Russian
Researchers at the HSE Centre for Language and Brain have presented RusLan-M, an open multimedia corpus that makes it possible to trace the development of early child speech in Russian from first words to the emergence of complex grammatical constructions. The database contains around 41 hours of video recordings and more than 35,000 child utterances. The new resource will help researchers study more precisely how children acquire Russian and, in the longer term, develop more reliable tools for assessing speech development. The study has been published in Language Resources and Evaluation.
Scientists Develop Algorithm for More Reliable Processors in Data Centres
Researchers from HSE MIEM and Samara University have developed the LRF-3D algorithm to automatically bypass idle nodes in three-dimensional networks-on-chip. Thanks to its hierarchical architecture, the algorithm outperforms existing solutions in both speed and path accuracy, improving processor reliability for use in data centres, supercomputers, and AI computing. The source code and test results are publicly available.
Researchers Rank Recommendation Algorithms Using Sports Tournament Model
Researchers from the AI and Digital Science Institute at the HSE Faculty of Computer Science have developed an approach for selecting recommendation algorithms more effectively. Their approach uses pairwise comparisons of algorithms to create a tournament table, with the overall ranking based on their performance across all datasets in the tournament. This can reduce the number of algorithms that need to be tested when developing new services, saving both time and money. The study was presented at the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2026).
Researchers Develop Method for Direct Generation of Regulatory DNA
Researchers at HSE University have developed a model for generating promoters and enhancers—DNA sequences that regulate gene activity. The model works directly with DNA nucleotides, without first transforming them into a continuous numerical representation. This solution could be useful for applications in synthetic biology and gene therapy. The study results were presented at the ICLR 2026 Workshop ‘Generative AI in Genomics (Gen^2): Barriers and Frontiers.’
Researchers at HSE University and Sber Train Neural Networks to Better Predict User Preferences
The HSE FCS AI and Digital Science Institute and Sber have introduced a new architecture for recommendation systems that combines two classes of models, enabling algorithms to better predict users’ interests and needs. A preprint of the paper has been published on arxiv.org and presented at Urban ML.
Physicists Discover What Happens Inside a Stable Vortex
Large vortices with characteristic spiral arms are often observed in the atmosphere and the ocean. Physicists from HSE University have explained how these structures form and why they retain their shape. The researchers found that velocities at points located along the same vortex arc remain correlated even over long distances. At the same time, this correlation weakens rapidly with increasing distance from the vortex centre. These differences help explain the formation of spiral arms and may improve models of atmospheric and oceanic currents. The findings have been published in Physical Review Fluids.