One Page That Gives People a Say: My Proposal at the UN
Cover image: AI-generated illustration of dialogue about technology with fictional people; not a photograph of the event.
Anyone receiving public funding to research or develop new technology should explain to the public, in understandable language, what they are working on. For me, one page would be the minimum. What problem is the technology meant to solve? What can it demonstrably do, where are its limits, and how can the people affected have a say?
I have been making this proposal for years. At the UN Digital Cooperation Day in New York, I raised it again. For me, it captures a practical task of Robotic & AI Governance: people need to be able to assess technology and express what they want while important decisions are still open.
An understandable text is the starting point. Participation begins when questions and objections receive an accountable response: who made the decision, and what changed as a result of the feedback?
In this article: first, the documented outcomes from New York and the original recordings; then my personal commentary and a practical proposal for publicly funded technology.
Event: 21 September 2026 · Research and source review: 18 October 2026
Part 1 · Report: What the UN Digital Cooperation Day Produced
On 21 September 2026, the UN Office for Digital and Emerging Technologies (ODET) held the second UN Digital Cooperation Day in New York. During the high-level week of the UN General Assembly, the event brought together three tasks: assessing scientific evidence, informing policy decisions, and building the capacity for wider participation in AI. The official programme documents the sessions and recordings.
The outcomes at a glance
Topic
What is available
Status and significance
National AI strategies
Introduction of the UN Blueprint for National AI and Data Strategies, drawing on experience from Lesotho and Guinea
A practical guide to implementing national strategies. Lesotho had already piloted it in August.
Scientific risk assessment
The first thematic brief from the Independent International Scientific Panel on AI
Released on 21 September as an advance unedited version; its focus is AI agents, misalignment and risks of losing human control.
AI and disaster risk reduction
A new partnership between ODET and the UN Office for Disaster Risk Reduction (UNDRR)
An announced collaboration on using AI to reduce disaster risk.
Work, open AI and cross-border testing
Discussions of skills, open-source AI and shared sandboxes for AI governance
Topics for exchange; a session alone does not amount to an adopted international standard.
National AI strategies: from policy ambition to practical planning
The Blueprint starts with a practical question: how can a national AI strategy become something a country can actually implement? In Lesotho, the work covered computing capacity, energy, connectivity, data, skills and financing. The August workshop produced a draft national roadmap for computing capacity. The New York launch brought together Lesotho's experience and Guinea as the next country to apply the approach. The ODET report describes that process.
AI agents: a scientific brief on risks and questions of control
The new thematic brief examines how the behaviour of capable AI agents can diverge from human intentions. It takes a documented incident during the safety evaluation of such systems as its starting point. The Panel places the incident in its research context and considers safeguards used in other fields with demanding safety requirements.
The limits of the assessment matter: the brief estimates neither the probability nor the timing of a severe future loss of control. It does not prescribe binding policy measures. The original publication is labelled an “advance unedited version”.
The preliminary overall report, released on 1 July 2026, and the new thematic brief serve different purposes. The overall report assesses the field more broadly; the September brief explores a particular risk question. The scientific Panel's FAQ explain this sequence of publications. My earlier assessment of the preliminary UN AI report remains available as a contribution written at that time.
Work, education and a shared evidence base
The session on the future of work focused on education, skills and the readiness of people and institutions. This included the question of who can participate in an AI-enabled economy. Recording: work and education.
The “Panel of Panels” compared international scientific assessments: where do they agree, where are the gaps in knowledge, and which perspectives are missing? Experiences from the Global South formed part of that discussion. The focus was on improving the evidence available for governance. Recording: Panel of Panels.
What can be recorded as an outcome
The day brought together a practical guide, a new scientific publication and an announced partnership. Other sessions provided opportunities to exchange experience on implementation and cooperation. The official event report places these steps in the context of implementing the Global Digital Compact. It does not document a decision adopting my one-page proposal.
Watch the official recordings
The UN programme links to three parallel recordings. The timestamps provide starting points for the main topics in the Oasis stream; the room links also give access to the other sessions.
The timestamps refer to elapsed video time and mark the beginning of each topic.
My initiative in the UN Portal: Robotics & AI 2050+
My Delphi study is now also listed as an initiative in the UN Digital Cooperation Portal: Robotics & AI 2050+. The direct entry in the MUDT/TUM profile describes a global, independent study of the future of robotics and artificial intelligence. It brings together expert assessments and covers six enabling technological capabilities and nine application domains.
The Portal maps the initiative to areas including international AI governance and inclusion in the digital economy. The study remains an independent research undertaking; the listing makes it discoverable in the international cooperation directory.
For me, these two tasks complement each other. The study aims to develop a systematic understanding of experts' views of the future. The one-page proposal aims to enable people outside the specialist community to contribute their own questions and expectations.
Part 2 · Personal Commentary: One Page That Gives People a Say
The following reflections express my own position. I raised the proposal at the event and want to put it up for discussion.
One proposal, several opportunities to raise it
I attended the UN Digital Cooperation Day in New York as an expert.
The visit included many interesting conversations with people who help shape the AI debate. I raised my one-page proposal in several settings: in conversations and in a contribution framed as a question to a panel.
My concern is a practical connection between research and the public. If we develop technologies that may change many people's everyday lives, those people need an understandable basis for forming a judgement. They also need to be able to raise their questions.
My proposal starts with something we can take responsibility for as researchers and developers: explaining what we do, what we know and what remains uncertain.
Guess who I bumped into in New York — and promptly roped into a selfie. Gary Marcus was a little caught off guard, but we had a lovely chat afterwards.
A scientific paper serves a different purpose from a public explanation
Scientific publications primarily address fellow specialists. They should make methods, results and conclusions open to scrutiny. That requires technical precision and the terminology of the field.
People outside that field need an understandable way in as well. They help fund research through their taxes. They encounter its applications in everyday life. Some benefit; others have to deal with consequences they did not choose.
That is why the proposal explicitly includes us as professors. We should be able to explain the problem we are working on, which expectations we consider justified and where we still have questions ourselves.
Writing one understandable page requires choices. What do people need to know to make sense of the project? Which terms need explaining? Which limitation must I avoid hiding behind an impressive demonstration?
Six questions for publicly funded technology
For me, the page should answer six questions.
Problem & people affectedWhat are we trying to solve – and for whom?
Technology & alternativesWhy this approach? What else could work?
Demonstrated capabilitiesWhat already works – and under which conditions?
Limits & what is unknownWhat can we not do, or do not yet know?
Risks & responsibilityWho decides on safeguards and when to stop?
Questions & participationHow can the people affected influence decisions?
1. What problem are we trying to solve, and who would benefit?
Start with the need. Who is meant to benefit from the project? What would an improvement look like? The explanation should also address who might face disadvantages or additional demands.
2. Why do we need this technology? What alternatives are available?
A technical possibility does not, by itself, justify its use. There may be a simpler way to solve the problem. A combination of technical and organisational changes may be needed. The public should be able to understand why a particular approach was chosen.
3. What demonstrably works already, and under what conditions was it tested?
A promise needs a basis. What has actually been tested? Under which conditions? How much of that can already be transferred to the intended application? Understanding the difference between an experiment and reliable operation helps people assess expectations.
4. What can we not do yet, and what do we not yet know?
An honest “We do not know yet” is a useful answer. It makes uncertainty visible. This includes technical limitations and open questions about the consequences of a possible deployment.
5. What risks do we see? Who decides on safeguards or stopping the project?
Identifying risks needs to affect the design. Who is responsible? What safeguards are planned? Who can intervene if assumptions no longer hold or problems arise? These responsibilities should be understandable too.
6. How can affected people ask questions and influence further development?
A contact address can be a starting point. What matters is what happens next: when can people contribute? Who answers their questions? How will they find out whether an objection changed a decision?
Imagine a publicly funded research project developing a service robot for public buildings. Its purpose is to help visitors find their way. This is a hypothetical example to illustrate the proposal.
The public explanation could first identify the navigation problem being studied and the people the support is intended to help. It would then need to explain why a robot is being tested for this purpose. Better signs, an accessible building map or a staffed information point would be obvious alternatives to compare.
To explain its capabilities, the project would need to describe what works so far and under which conditions. Does the system recognise differently worded questions? Can it handle several languages? What happens when it does not understand a question? Which tasks will a person continue to handle?
If the project needs sensors or personal data, the explanation should describe why they are needed and which questions remain open. It should also make clear how people can reach their destination without using the robot.
Finally, the project needs feedback from everyday use: from visitors, employees and people whose needs may have been overlooked in the first design. Their suggestions should still be able to influence the design.
That turns a general announcement into a project people can discuss in practical terms.
Basic research needs room for what we do not know
For basic research, the honest answer to the question of practical benefit may be: we do not know yet. Advancing knowledge is a legitimate aim. I do not want every scientific question to have to justify itself prematurely with a supposedly certain business case or an immediately measurable everyday benefit.
Even then, it is possible to explain the question being studied, why it is scientifically interesting and what uncertainties remain.
My proposal does not require publishing trade secrets or security-sensitive details either. A public explanation must respect those boundaries. Anyone promising a benefit should, however, be able to explain the basis for that promise and what is still an expectation.
The page opens the conversation
One page alone does not create participation. I therefore want it to come with an accessible contact person and an accountable way of handling feedback.
What questions were asked? What objections were raised? Who decided how to respond? What changed, what was deliberately retained, and why?
The point is not to accept every suggestion. Participation becomes credible when decisions are explained and people can see what their contribution achieved.
This approach connects my thinking on Robotic & AI Governance with the practical approach in AI After 60: explain clearly, make independent checking possible and help people form an informed opinion.
The same question arises in social media regulation for young people: how can protection, education and self-determination work together? Support must fit the people and their circumstances. Understandable information and meaningful opportunities to have a say remain fundamental tasks.
A pilot measured by how well people understand
The objection “more bureaucracy” is justified. My proposal is therefore to integrate the page into existing project reports and update it at important milestones. I do not want an additional form that ends up circulating only between the project and its funding body.
I would start by piloting the requirement in one funding programme. For me, that includes testing the explanation with people outside the field. Can they describe the purpose in their own words after reading it? Can they tell what already works and what is still uncertain? Can they find the right route for asking a question?
It would be equally important to assess whether feedback arrives in time to influence further development. Those are the questions by which I would judge the pilot.
International scientific cooperation remains important. My assessment of the UN's scientific AI Panel considers that wider level. An understandable explanation of a particular project gives people access to decisions that affect them directly.
I would like us to start. Anyone receiving public funding should be able to give the public an understandable account of their work. One page is a manageable first step. Its value will depend on the conversation and participation we build around it.
Frequently Asked Questions About the UN Outcomes and the One-Page Proposal
About the meeting and its outcomes
What was the UN Digital Cooperation Day 2026?
The UN Digital Cooperation Day 2026 was an event organised by the UN Office for Digital and Emerging Technologies on 21 September in New York. It focused on science, policy and capacity-building for a global AI future. Governments, researchers, businesses and civil society came together to exchange perspectives. The official programme sets out the context and individual sessions.
What concrete outcomes did the UN Digital Cooperation Day produce?
Documented outcomes include the launch of the UN Blueprint for National AI Strategies, the scientific AI Panel's first thematic brief, and a new ODET-UNDRR partnership on AI and disaster risk reduction. Topics discussed also included work, open AI and cross-border sandboxes. The UN event report and the thematic brief's publication page document these different kinds of outcomes.
Were new binding global AI rules adopted there?
The official materials reviewed here document practical guidance, scientific assessment, cooperation and discussion. They do not record the adoption of a binding global AI law or of my one-page proposal. The precise document type and its status therefore matter. A scientific assessment informs policy decisions; my proposal for publicly funded technology remains clearly marked as personal commentary. Sources: UN event report, the Panel's mandate and publications.
How does the September thematic brief differ from the preliminary UN AI report?
The preliminary report of 1 July 2026 assesses AI's opportunities, risks and impacts more broadly. The first thematic brief, released on 21 September, focuses on AI agents, misalignment and risks of losing human control. It explores one question in greater depth rather than simply replacing the overall report. The Panel explains its publication sequence in its own FAQ. My earlier blog post about the overall report retains its historical context.
What does the listing of Robotics & AI 2050+ in the UN Portal mean?
The UN Digital Cooperation Portal lists the global, independent Delphi study “Robotics & AI 2050+” as an active initiative in the MUDT/TUM profile. The entry describes its research approach and maps it to objectives of digital cooperation. This makes the initiative discoverable in the cooperation directory. The listing alone does not establish particular findings or the completion of a new survey round. The details are available in the direct UN Portal entry.
Where can I watch the UN event recordings?
Official recordings are available for Oasis, Bamboo North and Bamboo South. In the Oasis stream, the timestamps above lead directly to national AI strategies, work and education, the scientific thematic brief, and the Panel of Panels. To hear individual statements in context, viewers can continue watching the full stream from those points.
About my proposal for technology funding
What is a one-pager for publicly funded technology?
I mean one understandable public page about a research or technology project. It explains the problem being investigated, why the approach was chosen, what demonstrably works, where the limits and risks lie, and how affected people can have a say. After reading it, people outside the field should be able to ask informed questions. The page complements scientific publications by providing an accessible way in for the public. I also explain the proposal in my own video post.
Who should the one-page requirement apply to, and what should the page cover?
My proposal addresses anyone receiving public funding to research or develop technology. That explicitly includes universities and us as professors. I propose six answers: the problem and the people affected; the technology and its alternatives; demonstrated capabilities; limitations and what is not known; risks and responsibility; and opportunities to ask questions and influence decisions. This gives the public an understandable basis for judgement without requiring every reader to complete a specialist degree first.
How can basic research be explained when its eventual benefit is still unknown?
Basic research can explain its scientific question, why it matters and what remains open. A later practical benefit may be unknown. That is precisely what the page should then say in understandable language. I do not want to force a supposedly certain everyday benefit that the research cannot yet demonstrate. An honest account of what is not known helps the public distinguish new knowledge, expectations and substantiated results.
Would this require publishing trade secrets or security-sensitive details?
My proposal calls for an understandable explanation within justified limits of confidentiality. Goals, expected benefits, demonstrated capabilities, open questions and responsibilities can often be described without disclosing protected technical details. Where information has to be withheld, the public explanation should make that boundary understandable. The aim remains to enable people to assess a project and ask questions about it; the page is not a complete technical disclosure.
How could the one-pager be piloted without creating more bureaucracy?
I would initially integrate it into existing reports in one funding programme and update it at important milestones. What matters for the pilot is whether people outside the field can subsequently explain the project, recognise its limitations and direct a question to the right person. It should also assess whether feedback reaches the development process in time. For me, success means understandable information and workable routes to participation.
When does understandable information become meaningful participation?
Participation begins when questions and objections can influence decisions that are still open. That requires accessible contacts, suitable opportunities for dialogue and accountable responses. People should be able to see what changed because of their feedback, or why a decision was retained. Both technically informed objections and questions from people without specialist knowledge need a place. My articles on AI After 60 and Robotic & AI Governance develop this thinking further.
AI After 60: Try It, Think Critically, Have Your Say
Cover image: AI-generated illustration with fictional people; not a photograph of the Augsburg event.
Can I still get to grips with artificial intelligence after 60? I encountered that question at the Seniorentag 60+ event at Augsburg's adult education centre, the Volkshochschule, on 8 November 2025. My answer is yes. Start with something that interests you. Ask someone to explain an application, try it and check the results. This report on the Augsburg event provides the context for my talk.
I wanted to give people encouragement and a basis for forming their own opinions. Experiencing where a technology helps and where it falls short makes it easier to say what you expect from it. That takes understandable explanations and opportunities to try things out.
For me, the question therefore leads directly to participation in society. How do people gain access to new technologies? How can they influence their development? And what options remain for those who want to keep doing important things offline?
Your year of birth does not determine your openness
I know older people who are more open to new technologies than many younger people. That is a personal observation, not a statistic about generations. It reminds me to see people as individuals, with their own interests and circumstances.
Someone who has had little opportunity to explore digital applications may need more time, a clear explanation or someone alongside them for a first attempt. Good teaching makes room for that. It takes questions seriously and lets people decide for themselves what they want to use a technology for.
Looking at other age groups helps clarify the aim. My article on social-media regulation for young people also considers the relationship between protection, digital education and self-determined participation. Support needs to suit a person's age and circumstances. For me, enabling people to reach their own judgments connects the two debates.
Why I use dog training to explain AI
In my talks, I show an optical illusion: a wave pattern turns into the face of a young woman when you narrow your eyes. The familiar “dog or muffin” comparison also draws attention to how easily similar visual patterns can confuse us. These examples help me make pattern recognition tangible.
To illustrate one aspect of learning, I also use an analogy with dog training. Imagine a dog; let's call him Bello. He is learning to sit in response to a cue. His attention is drawn to a hand holding a small treat. The hand moves slowly over his head. Bello follows it, sits down and receives the treat along with praise. Dogs Trust also describes the basic principle of this training method.
Through repetition, a connection develops between the cue, the behaviour and a positive consequence. The hand movement can gradually be phased out. Eventually, the word “Sit” is enough. Bello has learned an association without having to follow a human explanation of the entire training situation.
For AI, the analogy illustrates how feedback can influence learning. In reinforcement learning, particular outcomes are evaluated, and the system is adjusted during training to make more highly rated outcomes more likely. The reward is a numerical quantity. The analogy explains this aspect of learning. Other AI training methods work differently, and it does not describe everything a dog can understand either.
The essential point for me is this: A convincing result alone proves neither human understanding nor subjective experience. AI can generate new texts, help with creative tasks and solve certain reasoning problems. Research shows, for example, improvements on verifiable tasks such as mathematics and programming, as well as support for writing stories. See DeepSeek-R1 in Nature and Doshi and Hauser on AI and creative writing.
A friendly turn of phrase therefore does not establish that the system has feelings of its own. And a confident answer does not establish that it is correct.
Start with something you want to do
In my Augsburg talk, I discussed smartphone translation features and help with research and planning, among other examples. I find concrete tasks like these useful starting points:
Have a short message translated, then compare a translation back into the original language with what you wrote.
Collect ideas for a day trip. Then check opening hours and transport connections with the relevant providers.
Ask for an explanation of a term and compare it with a reliable source.
Choose something manageable whose result you can judge. An abstract discussion then becomes your own experience: What helps me? What would I like to work differently? Where do I encounter limits?
If getting started feels difficult, trying something together can help. Your questions should guide the process. Someone with experience can support those first steps.
The key question: Does that make sense?
“Does that make sense?” is a simple question I keep asking when working with AI.
Outputs that sound plausible but are false or fabricated are often called AI hallucinations. I also discuss this limitation in my article “Blumentopf der KI” — why Germany still has a chance in AI. What matters in everyday use is this: an answer's tone does not tell us whether the information is correct. A verifiable source is therefore more useful than confidence alone.
Does the answer fit what I know? Can I check its source? Is the information still current? What do I need to confirm before acting on it?
A well-written itinerary does not confirm an opening time or a train connection. A plausible explanation deserves a second look when important decisions depend on it. Let AI make a suggestion, then check what matters for your purpose.
Trying things out and remaining sceptical go together. First-hand experience helps you form a considered opinion: I want to use this application. This result convinces me. Here I need more information or a different approach.
Understandable explanations make participation possible
People should be able to help shape technology. That is why I call for an understandable explanation of publicly funded technology: Anyone receiving public funding to research or develop technology should explain to the public, on one page, what they are working on. I have also made this one-pager proposal in connection with the UN Digital Cooperation Day in New York.
That page should address the questions people need answered to form an opinion:
What problem is being addressed, and who benefits?
Why was this technology chosen, and what alternatives are available?
What has demonstrably worked, and under what conditions?
What are the limits and unanswered questions?
What risks are involved, and who is responsible?
How can people affected by the technology ask questions and influence decisions?
An honest “We don't know yet” belongs on that page too.
The explanation opens the conversation. Participation also needs people who can be contacted and a clear account of what happens next: What changed because someone raised an objection? What stayed the same, and why? People must be able to contribute their wishes while decisions can still be influenced.
I address the international dimension of this task in my analysis of the United Nations' scientific AI panel. That article focuses on shared foundations of knowledge and how societies approach AI's opportunities, risks and unanswered questions. The call for an understandable one-page explanation is my own proposal.
For me, that is part of a participatory dialogue about technology and of Robotic & AI Governance. My analysis of Zuckerberg's AI vision leads to the same question: How do we connect personal opportunities with public responsibility?
Participation in society also needs offline options
I want to encourage people to try digital technologies. At the same time, I call for a right to participate in society through non-digital means. People who cannot or do not want to use a smartphone must retain access to essential information, services and opportunities.
If an appointment can only be booked through an app, or important information is available only through a QR code, there is a practical question to answer: How can someone access the service if they cannot use that route?
Digital services can open up access. Good design also takes account of personal conversations, understandable information and accessible offline options. In my view, someone's choice of device must not determine whether they can take part in society.
My message from Augsburg remains: Try the technology, check its results and form your own opinion. Make that opinion heard. Shaping our technological future concerns us at every age.
Yes. Choose a manageable task whose result you can judge for yourself. An understandable explanation and support suited to your needs can make getting started easier.
What are useful ways to try AI in everyday life?
A first attempt could be a short translation, an explanation or a collection of ideas for a day trip. Check the result; for plans, that includes checking opening hours and transport connections against the providers' information.
Can I trust an AI's answers?
A confident tone is not enough. Check important claims for plausibility, sources and currency. What matters for a decision is whether the relevant information is reliable.
What does the dog-training analogy explain about AI?
It illustrates one aspect of learning: connecting a cue, behaviour and feedback. It is not a complete account of every AI method. Good performance alone does not prove human understanding or subjective experience.
What does a participatory dialogue about technology mean?
People should receive understandable explanations of technology and be able to influence its design at an early stage. That includes a clear account of how their questions and objections affected the outcome.
Why do offline options matter as more services become digital?
Because participation in society should not depend on using a particular device. My political aim is to expand digital opportunities while preserving accessible non-digital routes.
Further reading, October 29, 2026: How curiosity about AI connects with professional competence, judgment and early-career training is explored in my article on AI and the future of work.
Social Media for Young People between Protection and Digital Education
Editorial status: 15 October 2026.
What research says about risks and restrictions, what Germany's government and the EU are proposing, and what a voluntary school mode for digital devices could look like. An evidence and policy review followed by my personal commentary.
During a panel discussion after my most recent keynote, one thought kept returning: young people need to learn how to navigate a digital world. At the same time, we must protect them from abuse, overwhelming experiences and services deliberately designed to keep them engaged. Once we discuss specific rules, this shared objective becomes a difficult balancing exercise.
A senior criminal investigator taking part, who said he also works in schools and prevention, spoke about cybergrooming and cyberbullying. I am paraphrasing his contribution here: young people need to understand which behaviours harm others and what legal consequences they can have. The internet must not be a space beyond the law. I agree. Psychological harm deserves attention just as physical harm does.
In the same discussion, I proposed what I call “Bayern-MDM”: a voluntary, clearly bounded school mode for suitable personal devices. Distracting applications would be restricted during the designated school periods, while research and guided digital work would remain possible. For me, this also includes age-appropriate AI tools approved for school use. The technical and legal requirements are part of the proposal.
The debate has a new political impetus. On 14 October 2026, Germany's Federal Cabinet adopted policy principles for protecting children and young people in digital media. With the proposed KIDS Act, the European discussion also goes beyond a single age threshold. Adopted policy principles, a legislative proposal and law already in force are different things. Federal Cabinet's list of decisions, publication of the EU proposal.
The scientific and policy review comes first. In the commentary that follows, I set out my position and my proposal for Bavaria.
Panel discussion on social media, digital education and the protection of young people. From left to right: Markus Mayer (moderator), Mark Schmid, Sebastian Menzl, Dominik Bösl, Peter Tomaschko and Christina Mößle. Photo supplied by the author.
Research on use and health
Make the right distinctions before citing the first number
“Screen time” covers very different activities. A digital school assignment, a video call, a computer game and a personalised short-video feed are different experiences. Social media use can likewise mean talking to friends, creative work, looking for information or repeated exposure to distressing content. Research therefore examines both time spent and patterns of use and specific experiences. A study of all leisure screen use answers a different question from a study of personal platform accounts. National Academies, Social Media and Adolescent Health.
The distinction between intensive and problematic use is equally important. The WHO's description of problematic patterns includes loss of control, neglect of other activities and negative consequences in everyday life. A high number of minutes alone cannot replace that assessment. WHO, Teens, screens and mental health.
Studies can also answer different kinds of questions. A cross-sectional study identifies associations at a particular point in time. A longitudinal study follows changes and sequences over time. A randomised study can provide stronger evidence about the effect of a specific intervention. None of these methods automatically makes its findings transferable to every age group and policy measure. The following examples show why those distinctions matter.
Associations exist, and their size and direction vary
An umbrella review published online in eClinicalMedicine on 4 October 2026 brings together 121 systematic reviews covering around 2,790 distinct primary studies and more than nine million participants in total. It examines digital media from birth to age 24. Many associations are unfavourable, particularly for problematic patterns of use. However, results differ by content and life stage; age-appropriate educational content and certain active forms of use can be associated with more favourable outcomes. The certainty of the evidence is predominantly rated low or very low. The large total should therefore be read neither as blanket reassurance nor as clear proof of causation. The review covers reviews up to 2025, rather than a new October survey of nine million people. eClinicalMedicine, umbrella review and meta-analysis.
A systematic review by Fassi and colleagues, published in 2024 and corrected in 2026, includes 143 studies with approximately 1.09 million young people in total. The pooled correlation between time spent using social media and internalising mental health symptoms, such as depressive symptoms and anxiety, was approximately r = 0.12. That particular subset comprised 56 studies with around 482,000 participants. This is a small statistical association, with substantial variation between studies. It means neither that social media makes twelve per cent of users ill nor that severe individual difficulties can be ruled out. The predominantly observational data do not, on their own, establish general causation. Fassi et al., JAMA Pediatrics, current version.
In their 2024 report, the National Academies concluded that the evidence then available did not adequately support a broad causal explanation of adolescent health trends at population level. The report also describes specific risks and potential benefits. That assessment remains an important methodological reference point; newer findings need to be considered alongside it. Report summary.
One such addition is the 2025 longitudinal study by Nagata and colleagues, involving 11,876 children whose average age at baseline was just under ten. Increases in an individual's social media use relative to their own average preceded higher depressive symptoms in subsequent observation intervals. This temporal sequence strengthens the indication of a risk. Since use was not randomly assigned, other influences remain possible explanations. The finding is not a rate of newly diagnosed depression. Nagata et al., JAMA Network Open.
Another paper, published in JAMA in 2025 and involving 4,285 adolescents, examined trajectories of addictive use. High or increasing problematic patterns were associated with poorer mental health outcomes. This directs attention towards loss of control and disruption to everyday life. Here, too, the questionnaire constructs used are not individual clinical diagnoses, and the association does not identify a sole cause. Xiao et al., JAMA.
What frequently cited percentages actually mean
The 2022 WHO/HBSC survey, whose results were published in 2024, covered almost 280,000 eleven-, thirteen- and fifteen-year-olds across 44 countries and regions. Eleven per cent showed signs of problematic social media use, compared with seven per cent in 2018. These figures describe that particular international survey. They are neither a German diagnosis rate nor a measurement from 2026. At the same time, intensive users without problematic patterns reported stronger support from peers. WHO analysis.
For Germany, the 2026 DAK/UKE study provides a different perspective. Its representative subsample comprised 1,005 families with children aged ten to seventeen, surveyed between 24 September and 12 October 2025. A total of 6.6 per cent met the questionnaire criteria used for pathological social media use. The increase from the preceding wave was not statistically significant. In this report, “problematic” includes both risky and pathological patterns; these categories must not be conflated. Different instruments and samples also prevent direct comparison between the German percentages and the WHO figures. DAK/UKE, findings and methods.
For policy assessment, the implication is straightforward: every number needs an age group, a data collection period and a definition. Otherwise, a finding that deserves serious attention becomes a misleading headline.
Restrictions can help, and must be assessed specifically
A randomised family study from Denmark provides a particularly interesting finding. Eighty-nine families with 181 children and adolescents were assigned to an intervention or control group. The intervention reduced leisure screen use to no more than three hours per week per person for two weeks; smartphones and tablets were handed over. The predominantly younger children subsequently showed more favourable parent-reported behavioural scores. The difference in the overall score was 1.67 points in favour of the intervention. This supports a short-term effect of that particular package of measures. Its short duration, parent reporting and comprehensive screen reduction limit its transferability to long-term government bans on social media. Schmidt-Persson et al., JAMA Network Open, 2024.
The 2025 SMART Schools study compared 1,227 twelve- to fifteen-year-olds at 30 English schools. More restrictive rules were associated with less phone and social media use during school hours. There was no corresponding difference in total use across weekdays and weekends or in mental wellbeing. This was a cross-sectional comparison, without random allocation of schools. It establishes neither that every restriction is ineffective nor that school bans produce a health benefit. Goodyear et al., original study.
A 2026 follow-up economic analysis adds a practical perspective. In the complete subsample of 20 schools with 815 pupils, differences in quality of life and wellbeing were small. Restrictive rules might, however, reduce staff time and costs; the cost estimate was uncertain. A rule can therefore matter organisationally even when a health benefit has not been demonstrated. Perry et al., BMJ Mental Health.
A US working paper by Allcott and colleagues, revised in September 2026, examines lockable phone pouches. It links different data sources and uses staggered introduction for a quasi-experimental comparison. It finds markedly less use at school. Average test-score effects are close to zero but vary by school stage; discipline and wellbeing follow different trajectories over time. This is recent research still presented as a working paper. A particular storage solution should also not be equated with every form of school phone ban. NBER, Working Paper 35132.
What a finding can establish depends on the measure and method. Editorial analysis based on Fassi et al., Schmidt-Persson et al. and Goodyear et al.; detailed sources in the text. As of 15 October 2026. Sources: Fassi et al., Schmidt-Persson et al., Goodyear et al..
Sleep, education and young adults
A 2026 meta-analysis of daily changes in screen use and sleep includes 25 studies with 4,562 participants aged three to twenty-five. Higher-than-usual use was, on average, weakly associated with later sleep onset; other pooled sleep measures showed no statistically significant overall associations. The analysis examined screens in general and short-term changes. For regulation, this suggests considering timing and specific functions as well. JAMA Pediatrics, original abstract.
For young adults, a 2025 study of 373 eighteen- to twenty-four-year-olds is informative. Of these, 295 chose a one-week social media break; several mental health symptom scores improved. Self-selection and the absence of a randomised control group limit the conclusions. The finding supports further investigation of self-regulation, but establishes neither a lasting effect nor the effect of a government ban. Calvert et al., JAMA Network Open.
Education, too, must be judged by its effects. A randomised study published in September 2026, involving 1,012 thirteen- to twenty-nine-year-olds, examined health warnings. Some warnings were perceived as more effective than neutral messages. The outcomes measured were perceptions and intentions, rather than long-term behavioural change. Those advocating media literacy should therefore ask for measurable outcomes just as they would for a ban. Grummon et al., JAMA Health Forum.
A review of legislative approaches published in September 2026 assesses 39 empirical papers, policy analyses and other contributions. Its search ends in March 2026. It identifies both potential approaches and gaps in research on effects. The number of publications does not mean that 39 controlled studies have validated a particular ban. Nagata et al., Evidence for Social Media Legislation Strategies.
A scoping review published in Communications Psychology on 26 September 2026 examines 45 studies of social media reduction and detox interventions from 2015 to 2025. It describes substantial methodological limitations. One point is particularly relevant to policy transfer: only one included study had a mean participant age below sixteen. This does not mean that no minors were studied. It does show how cautiously findings from predominantly older samples must be applied to younger children. The review examines intervention research; it does not thereby disprove every observed health risk. Calvert et al., scoping review of the strength of detox evidence.
Online abuse and the limits of criminal law
The health debate captures only part of the problem. Protection from abuse also concerns perpetrators' behaviour, accessible help and enforcement of existing rules.
Germany's police crime prevention service explicitly explains that cyberbullying is not a standalone criminal offence, but may involve offences such as insult, threats or defamation. Violations of personality rights may also be relevant. Whether a particular act is criminal depends on the statutory requirements. The threshold of psychological harm mentioned in the discussion therefore expresses an important protective principle, rather than a universally applicable legal definition. Police crime prevention service, legal explanation.
Section 176b of Germany's Criminal Code, StGB, is among the provisions relevant to preparing the sexual abuse of children. In policy discussions, “cybergrooming” should not be used to describe any unpleasant contact. It concerns a specific set of serious abusive behaviours that prevention and law enforcement need to address directly. Current statutory text.
A minimum age for accounts addresses these issues only partly. Safe contact settings, accessible reporting channels, prompt handling and support for those affected are also necessary. The European Commission identifies precisely these elements in its guidelines on protecting minors. DSA guidelines, 14 July 2025.
Germany and the EU in October 2026
What the Federal Cabinet decided on 14 October
Germany's government adopted policy principles for protecting children and young people in digital media. As a national interim measure until European age rules are established, social network use would generally be prohibited below thirteen, with exemptions for demonstrably child-appropriate services. Graduated safeguards are planned for older minors. This is a concrete political mandate for implementation; it does not itself bring a general age law into force. Federal ministry press release.
The package combines age rules with providers' responsibilities and empowerment. Planned measures include media education, support for parents and a “KI-Seepferdchen”, an introductory AI skills programme for primary-school children. The “d-you” digital identity wallet planned for 2027 is intended to enable age verification with minimal data disclosure; its use is to remain voluntary. Implementation is subject to allocation of responsibilities and budget constraints. Policy principles, especially pages 2–7.
The government's stated approach therefore uses several instruments. Criticism suggesting that it relies exclusively on bans would misrepresent the package. The details, funding, responsibilities and effects need to be examined.
On 7 October, the Cabinet had already adopted a bill against online abuse, covering issues including sexualised deepfakes, cyberstalking and civil enforcement. Here, too, adopting a draft does not mean the legislative process is complete. Federal government, bill against online abuse.
Existing European duties
The Digital Services Act, DSA, is already in force. Article 28 requires covered online platforms accessible to minors to take appropriate and proportionate protective measures. Profiling-based advertising is prohibited where a platform knows with reasonable certainty that the person concerned is a minor. These duties do not additionally oblige platforms to process personal data solely to establish age. The scope and exemptions, including for certain smaller providers, still need to be considered. DSA, particularly Articles 19 and 28.
The July 2025 guidelines explain possible safeguards: private default settings, safer recommender systems, restrictions on features designed to keep users engaged and better reporting channels. The guidelines provide voluntary guidance on meeting legal obligations; following them does not automatically guarantee compliance. They are not an already applicable general EU ban on accounts. Commission explanation of the DSA guidelines.
What the proposed KIDS Act would change
The European Commission presented its proposal for the EU KIDS Act on 17 September 2026. As of 15 October, Parliament's register still lists the procedure at the preparatory stage. Parliament and the Council must first adopt the final legal act. A headline such as “The EU has banned social media for children” misrepresents this stage of the process. Commission proposal, European Parliament procedure register.
For certain social networks and video platforms with risk-related features, the proposal provides for graduated accounts:
Age
Proposed access
Under 13
No personal account; a narrowly defined option for specially designed video services would remain available through a parent or guardian's account.
13 and 14
A restricted account set up by a parent or guardian, with continuously active safeguards, a maximum of one hour's use a day and approved contacts.
15 and above
An independently held account; protection of minors remains relevant until adulthood.
The proposal also includes safe-design requirements addressing features such as endless engagement loops and unwanted contact. Further requirements also concern games, app stores and AI systems. Age rules for certain accounts therefore do not amount to a ban on every digital learning and communication service. Commission explanation of the KIDS Act, proposal, particularly Articles 6 and 7.
Law in force, the EU proposal and Germany's policy principles must be assessed separately. Editorial overview based on the official sources linked in the text. As of 15 October 2026. Sources: DSA, EU proposal, German policy principles.
International models and positions
Australia and early experience with an age rule in force
In Australia, covered platforms have been required since 10 December 2025 to take reasonable steps to prevent under-sixteens from holding accounts. Responsibility and potential penalties fall on providers, rather than children or parents. Certain services are exempt; reading publicly available material without an account is distinguished from holding a personal account. eSafety, current explanations.
The first published evaluation presents a mixed early picture. Three months after implementation, the proportion of under-sixteens in the study holding a social media account fell from 52.4 to 42.1 per cent. Use with or without an account fell from 85.9 to 81.5 per cent. The data were collected in March and published in July 2026. The regulator stresses that this early snapshot does not yet allow a robust assessment of the longer-term intended outcomes. Blocked accounts and improvements in health are different measures of success. eSafety, first evaluation of 31 July 2026.
France and the question of proportionality
On 14 August 2026, France's Conseil constitutionnel declared the general under-fifteen access rule in Article 1 of a law unconstitutional. Among other points, the decision criticises the broad coverage of services with different risk profiles, insufficient individual differentiation and inadequate safeguards in age verification. At the same time, the court recognises that protecting children can, in principle, justify access restrictions. The decision therefore establishes neither a French blanket ban currently in force nor a general prohibition of all regulation. Nor does it automatically determine the legality of the EU proposal. Decision 2026-911 DC, particularly paragraphs 10–22.
Denmark and Norway as further policy models
In September 2026, Denmark's government said it would tighten its proposal to an age threshold of fifteen. An earlier proposal allowing parental approval from thirteen therefore no longer describes the newer proposal without qualification. Stricter rules on personal screens in schools were announced at the same time. The government's statement establishes its policy direction, rather than demonstrating that legislation is already fully in force. Danish Prime Minister's Office, press conference of 10 September 2026.
In April, Norway's government announced a bill for 2026. Access would become possible from 1 January of the year in which a child turns sixteen. This differs from a strict prohibition until a child's sixteenth birthday: an entire year group would gain access together, some while still fifteen. Here, too, the cited source is a dated government plan. Norwegian government explanation of the model.
These examples show different decisions about thresholds, exemptions and responsibilities. A tally of “countries banning social media” would obscure those differences.
UN institutions connect protection with children's rights
There is no single position representing every UN body on every national age threshold. In General Comment No. 25, the UN Committee on the Rights of the Child connects protection with information, access, privacy and participation. Children's evolving capacities and independence should be considered. Restrictions must be lawful, necessary and proportionate; strong age controls for prohibited services remain expressly possible. This is an interpretation of the Convention on the Rights of the Child, rather than a newly adopted worldwide social media law. General Comment No. 25, particularly paragraphs 19–21, 59, 111 and 114.
UNICEF cautions against expecting age limits alone to provide adequate protection. Safe design, moderation, support for families and participation by young people form part of its recommended comprehensive approach. Movement towards less protected services also needs to be considered. This institutional assessment is not experimental proof that every age rule must fail. UNICEF statement, December 2025.
In its June 2026 family guide, UNESCO emphasises media and information literacy and support for parents. These complement regulation and safe design. This perspective is useful because it considers everyday life after a policy decision: rules need to be understood, supported and implemented. UNESCO, family guide for the digital age.
The policy options available
The following options can be combined. This comparison sets out their respective aims and unresolved assessment questions, drawing on the research and regulatory models described above. It is an analytical synthesis.
Instrument
Aim and potential strength
Key assessment question
Minimum age for accounts
Delay access to certain services involving risks and establish a shared expectation
Which services, exemptions and age checks are proportionate; do risks actually decrease?
Graduated youth accounts
Allow more access as independence grows
Do safeguards work under different family circumstances?
Safe design
Address risks in contact functions, recommendations and mechanisms designed to keep users engaged
How are duties assessed and enforced against providers?
School use rules
Protect lessons, concentration and face-to-face interaction
Which rules help in everyday school life, and how much staff time do they require?
Managed learning environments
Keep digital tools available for specific purposes while limiting distractions
Which devices are suitable; how are private life and equal access protected?
Media education and guidance
Develop source evaluation, self-regulation and safe behaviour
What can young people do better afterwards, and how does support remain accessible over time?
Prevention, reporting and enforcement
Prevent, stop and pursue specific abuse
Can those affected obtain help quickly, and do the responsible bodies respond effectively?
A sound decision must specify its objectives. Fewer accounts, fewer classroom disruptions, less exposure to abuse and better self-regulation are different outcomes. Each should be assessed using appropriate data. Privacy, participation and burdens on families and schools belong in the same assessment.
My commentary on protection and digital agency
Using digital tools with guidance and checking results together. AI-generated illustration with fictional people; not a photograph of the event described.
Schools need clear rules and an educational mission
I find it educationally contradictory to expect digital competence from young people while failing to give them sufficient opportunities to develop it with guidance. Independence is expected as they move into training, higher education and work. It develops through experience, practice, feedback and increasing responsibility.
For me, that does not imply an entitlement to uninterrupted personal platform use during lessons. Digital education must show how to research, check claims, work with AI outputs, protect personal data and interact with other people. That requires suitable learning environments, prepared teachers and time.
Bavaria's existing rules already provide room for this. Article 56(5) of the Bavarian Education Act, BayEUG, permits digital devices during lessons and in individual cases with the supervisor's permission. Permitted applications can be specified. For years one to seven, general authorisation outside these situations is excluded. This restricts personal use while allowing educational use. Claiming that Bavaria prohibits digital education altogether would be wrong. Current statutory text, effective from 2 October 2026.
For me, the policy task therefore lies in reliable implementation. Which skills are actually being taught? Which tools are available for that purpose? And how do we prevent an easily communicated restriction from receiving more attention than the more demanding work of creating good learning conditions?
The connection with Peter Tomaschko
In his published account of his July plenary speech, Peter Tomaschko emphasised protection in relation to personal phone use at school. His public position on schools offers a concrete starting point for discussion. I do not infer from it that he personally supports any particular general minimum age for social media. Tomaschko's account of his plenary speech.
On Monday, 19 October, I will discuss these issues with him again on a panel. I am interested in the practical connection between protection, teaching and growing independence. We should discuss that connection as concretely as the age printed in a draft law.
What I mean by Bayern-MDM
My proposal starts with an everyday school situation: during a learning period, a suitable device should be a work tool. The intended applications remain available. Distracting games and personal social media services are restricted in this mode. Outside the specified times, the school's intervention ends.
MDM stands for Mobile Device Management. Depending on the device and management method, a school can use it to provide learning applications and settings, among other things. My proposal is to develop a reliable, understandable and voluntary framework for this kind of school use in Bavaria.
It would be wrong to present device management itself as a new idea. The Bavarian Ministry of Education already describes MDM, including for pupils' personal devices. Its guidance covers consent, limited management methods, time-limited restrictions and loan devices as an alternative. Schools can make the use of personal devices conditional on enrolment; nobody has to enrol their own device. Which data are processed depends on the specific system. Ministry of Education, Mobile Device Management.
The further proposal is therefore to build on existing approaches, with understandable minimum standards, available solutions, support for schools and an independent pilot. An additional product name alone would improve nothing.
Technology imposes real limits
The idea of “install an app and all private distractions are blocked at school” cannot automatically be implemented across different personal phones. A central distinction is between a fully managed school device and a personal device with a limited school-managed area.
With Android work profiles, personal apps and data are generally kept separate from the managed area. The organisation manages the work area. This does not imply blanket control over every personal application. Google, Android Work Profile.
Apple's User Enrollment for personal devices likewise permits only a limited set of management functions. Its documentation separates school- or organisation-managed data and accounts from the personal account; other enrolment methods allow more extensive intervention. Device type, operating system and management mode therefore need to be assessed before making a commitment. Apple, User Enrollment and device management.
Bayern-MDM would need to explain these differences openly. For some requirements, school devices or dedicated classroom tools might be the better solution. Technical compatibility, reliability and the effort involved in switching between school and personal use belong in a pilot. The proposal is therefore a development task whose feasibility and effects can be tested.
Voluntary participation needs a workable alternative
During the discussion, I suggested that pupils who do not participate should put their personal phones away during school hours. A developed approach needs one further condition: choosing against school management of a personal device must not worsen access to digital lessons. A suitable loan device or an equivalent school-provided solution therefore belongs in the model.
This is also a question of genuine choice. The European Data Protection Board points out that, where there is a power imbalance, particularly in relation to public authorities, the freedom of consent requires especially careful assessment. Agreement to device management does not automatically settle every question about the legal basis for each processing operation. EDPB, Guidelines 05/2020 on consent.
I consider the following requirements essential for a pilot:
A clear educational purpose. Management covers what is needed for the agreed lessons. Private messages, photographs and contacts must not become subject to school content monitoring.
Visible, limited operating periods. Those affected can see when restrictions apply. Personal use outside these periods remains protected; continuous location tracking is not the objective.
Access without a personally owned managed device. Anyone without a suitable phone, or who chooses not to participate, receives a workable school alternative.
Security and support. Responsibilities, permissions, faults, deletion and leaving the scheme are clarified before it starts. Teachers need reliable support.
Participation and independent evaluation. Pupils, parents, teachers, school authorities and data protection specialists help design the pilot. Concentration, skills, workload and unintended consequences are assessed.
These requirements are my elaboration of the proposal. They do not replace the technical and legal assessment of the actual model selected.
Bayern-MDM as proposed by Dominik Bösl, with editorial elaboration: equal access, limited intervention and an independent pilot. This is not an established state programme; technical and legal assessment is still required.
From primary school to young adulthood
I want the debate to cover the whole developmental journey. At primary school, children can practise assessing information, recognising advertising, protecting personal data and seeking help with unpleasant contacts in protected learning environments. This does not require an unrestricted personal account on a platform involving risks.
In early adolescence, guidance that respects privacy while remaining available becomes more important. For roughly ten- to fourteen-year-olds, the American Psychological Association recommends viewing together, discussion and coaching; independence can increase as skills develop. This professional guidance does not establish a universally optimal statutory age threshold. APA, Health advisory on social media use in adolescence.
In later adolescence, greater independence should be accompanied by demonstrable competence. Rules must remain understandable and must not leave the transition to personal responsibility to chance. Young adults still need transparent services, good information and accessible support. They are adults; rules for minors cannot be transferred to them unchanged.
These four life stages offer guidance for education and policy. They do not imply that everyone reaches the same level of maturity on the same birthday.
Digital agency develops through guidance, practice and growing independence. AI-generated illustration; the life stages provide educational guidance.
Provider duties and education need lasting support
Education must not mean leaving families to struggle alone against the design of large platforms. I consider enforceable, verifiable duties for providers necessary. These include safe defaults, understandable recommendations, restricted contact options and functioning complaint channels. Whether a proposed rule achieves these aims depends on its design and enforcement.
At the same time, a rule on paper is insufficient to develop digital agency. Schools need time and support, families need understandable information, and those affected need help. Young people should participate in shaping the rules. To me, participatory technology dialogue means taking them seriously as people affected by technology and as its future shapers.
I will therefore bring a concrete proposal to the next panel: expand existing opportunities for digital education, pilot a voluntary school mode under clear conditions, and examine its effects openly. Protective rules deserve the same care. Ultimately, young people should be able to act more safely, recognise risks more effectively and exercise greater agency over the technology shaping their everyday lives.
Frequently asked questions about social media regulation
Has the EU already banned social media for children?
As of 15 October 2026, the KIDS Act is a proposed regulation. The parliamentary procedure is at the preparatory stage; the final text still needs to be adopted. The Commission's proposal is not a general ban already in force. EU procedure register.
What age groups does the KIDS Act provide for?
For covered social networks and video platforms, the proposal generally provides for no personal accounts below thirteen, restricted parent-set-up accounts for thirteen- and fourteen-year-olds, and independent accounts from fifteen. Safe-design requirements and specific exceptions also apply. Commission explanation.
What did Germany's government decide on 14 October?
The Cabinet adopted policy principles covering planned age rules, platform responsibility and skills development. A national interim rule is planned for under-thirteens, with exemptions for child-appropriate services. Policy principles do not themselves constitute an age law immediately in force. Official press release.
Does research show that bans are ineffective?
The research presented here does not support such a blanket conclusion. Family interventions, school rules and government account-age restrictions are different measures with different outcomes. Less use also does not automatically demonstrate a lasting improvement in health. Review of legislative approaches.
Is cyberbullying a criminal offence?
In Germany, cyberbullying describes different behaviours and is not a standalone criminal offence. Individual acts may constitute offences such as insult, threats or defamation and infringe other rights. The possible legal consequences depend on the particular case. Police crime prevention service.
Does Bavaria ban digital devices in lessons?
Devices are permitted during lessons and in individual cases with the supervisor's permission. For years one to seven, general authorisation outside these situations is restricted. The rules distinguish guided use from personal use. Article 56(5), BayEUG.
What does Bayern-MDM mean?
Bayern-MDM is my proposal for a voluntary school mode on suitable devices: learning tools remain available, distractions are limited, and school intervention ends outside the agreed times. MDM already exists in Bavaria. The proposal concerns a reliable framework, practical support and a pilot. Existing Bavarian MDM guidance.
Could a school use it to block every personal app?
That depends on the device and management mode. Android personal work profiles and Apple's User Enrollment limit access to private areas. A universal blocking app for every personal phone is therefore not guaranteed. Google documentation, Apple documentation.
Would children without a managed personal device be excluded from digital learning?
My proposal requires an equivalent school-provided alternative. Requiring a personal device to be put away and ensuring continued participation in digital lessons are compatible requirements. Consent, access and actual implementation must be assessed together as part of the model.
Is education an alternative to all regulation?
I support combining education, protection and verifiable provider responsibility. UNICEF likewise recommends a comprehensive approach and cautions against expecting age limits alone to provide sufficient protection. Media education must be assessed for its practical effects just as restrictions must. UNICEF statement.
Related reading added on 18 October 2026: Digital agency matters at every stage of life. In AI After 60, I apply the ideas of understandable explanations, first-hand experience and participation to older adults. Support needs to suit each person’s circumstances.
4 Robotic Revolutions: 10 Years On
4 Robotic Revolutions: 10 Years On
First published: 11 August 2024 (German original); this English version was published on 25 July 2026. Factual corrections and update: 4 October 2026.
Update, 4 October 2026: The following market data and FAQs reflect the July version. In World Robotics 2026, IFR reports a worldwide stock of five million industrial robots for 2025. The 2026 service robotics report has also been released. The AI Act omnibus amendment entered into force on 27 July 2026; the later legal position is explained in the dated update to the AI Act article.
Ten years after I first proposed the „4 Robotic Revolutions", the fourth revolution has stopped being a keynote promise. It has become an operations problem. I proposed the four phases in 2016 to bring order into a debate that treated every product launch as a revolution. On July 21, 2026, I used my opening keynote at the A3 Technology Transfer Congress in Augsburg to draw a first anniversary balance sheet. This article is the long-form version of that balance sheet. It is written not from the perspective of a demo lab, but from the perspective of the factories, hospitals, warehouses and lecture halls in which the four revolutions are actually competing for oxygen.
Stand in a workshop where a cobot works next to a human operator without a fence for the first time, and you understand immediately why categories matter. The cobot is not the event. The operator is not the event. The event is the process decision behind them: who is liable when the sensor misreads. Who stops the cell when the AI path planner picks a trajectory that looks unfamiliar to the human. Who trains maintenance to read adaptivity as designed behaviour rather than as a defect. These are the questions that I baked into the four phases of robotics a decade ago, because I had learned in KUKA innovation management, later at Festo, at Deutsches Museum and across many factory visits that technical readiness collapses on contact if organisational readiness is missing.
Why four revolutions in the first place
The idea was constructed by analogy with the evolution of the computer. Computers became smaller, then mobile, then ubiquitous, then invisible inside processes. Miniaturization, mobilization, ubiquity and pervasiveness described the move from the data centre to the smartphone to the embedded sensor. Robotics repeats those moves, in a different sequence: first the cage, then sensitivity, then mobility, then cognition. Together with Bernd Liepert, I proposed the four-phase model at IEEE IROS 2016 in Daejeon. The paper is titled „4 Robotic Revolutions - proposing a holistic phase model describing future disruptions in the evolution of robotics and automation and the rise of a new Generation R of Robotic Natives". The A3 region press release for the 2026 congress captured the core thesis in one sentence: four robotic revolutions, and the fourth is only just beginning (region-a3.com).
The phases are not a strict chronology. They overlap inside every plant, sometimes inside the same cell. Walk into a large factory today and you will find the first revolution in the body shop, the second in final assembly, the third in intralogistics, and the fourth in quality inspection and testing. The map is a maturity structure, not a timeline. To assess where an organisation stands, ask how the four phases relate to each other, which of them dominates, which is missing, and which is being underestimated.
First revolution: the caged robot
The first revolution starts with a safety philosophy, not with a model. The robot goes behind a fence because its motion is too fast, too strong and too deterministic to accommodate human exceptions. In 2016 I described the core of this phase as „how to bolt, weld and glue a car together as fast as possible". The blunt formulation still holds. Anyone who has spent time in early Rüsselsheim, Wolfsburg or Toyota plants knows: the first revolution was an industrial programme, not a scientific one. Its successes were cycle time, process stability and the elimination of degrading physical labour.
The zoo, as I called those installations, was historical rather than chosen. The first generation of industrial robot arms dates back to the 1960s: Unimate entered service at General Motors in 1961, Kawasaki began production in Japan in 1969, and ASEA (later ABB) introduced the IRB 6 in 1974. The industrial premise was simple: human labour at the edge of tolerable physical strain had to be replaced by a reproducible, monotonous, exact motion. The side effects were high capital intensity, low flexibility, and a programming paradigm that created an entire profession, the robotics technician.
What tends to be forgotten: this phase remains our largest segment by volume. The International Federation of Robotics reports in World Robotics 2025 that 542,000 industrial robots were installed worldwide in 2024, the second highest annual figure in history. The operational stock reached 4.66 million units by the end of 2024, a nine percent increase year-on-year. The first revolution is not history. It is foundation. And it continues to scale because the protected workspace limits direct contact between people and machines. These installations also require a risk assessment and a sound safety concept.
Second revolution: the cobot leaves the cage
The second revolution is the one the general public got used to fastest. A cobot on a final assembly line looks harmless, almost like a better manipulator in a friendly coat of paint. Behind it, however, stands a research programme going back thirty years, driven by Oussama Khatib at Stanford, Gerd Hirzinger at the Institute of Robotics and Mechatronics at DLR, and an entire generation of force-torque-sensor developers. In 2016 I argued that market readiness only arrived around 2015, with KUKA LBR iiwa, the Universal Robots UR family and Rethink Robotics Baxter as the frontrunners. A decade later cobots are an established category. The IFR 2025 report documents that installations of collaborative robots continue to grow at double-digit rates worldwide.
What the second revolution really changed is not the sensor. It is the legitimacy of letting humans close to the machine. Sensitivity - force, torque, speed, redundant path planning - is the technical precondition. The genuine shift lies in safety philosophy: instead of relying solely on spatial separation by a fence, safety-related system functions can enable collaboration. The safety of the application must be assessed, including its tool, workpiece and specific task. This enables new cell layouts, new process designs and, more importantly, new roles inside teams. The operator is no longer a feeder for the robot. She is a process partner.
Honesty requires a caveat: in many German factories, cobots are still operated as if they were caged. They sit inside small safety zones, guarded by light curtains, and are not deployed in genuine collaboration. This is less a matter of technology than of risk assessments under DIN EN ISO 10218 and ISO/TS 15066, and of a shop-floor practice that does not always separate adaptivity cleanly from the formal approval framework. Real collaborative applications remain rare. But they were the door opener - and without the door opener of the second revolution, the third would not exist.
Third revolution: the robot comes to the workpiece
The third revolution inverts a basic assumption of automation. Until then, we brought the workpiece to the robot. In the third phase, the robot comes to the workpiece. Autonomous mobile platforms, mobile manipulators, AMRs and intralogistics robots change not only layouts, but investment decisions: any manufacturer that produces flexibly can only afford rigid cells for high-volume processes.
The IFR 2025 report documents a strong market push in autonomous mobile robots and in transportation and logistics. The service robotics statistics for 2024 count about 200,000 new professional service robots worldwide, more than half of them in transportation and logistics - 102,900 units in that single category, up 14 percent. In 2024, the RaaS fleet in transportation and logistics grew by 42 percent; across all surveyed application areas, it grew by 31 percent. These are not just numbers. They are an answer to the arithmetic that mid-sized companies have been working through for years: shorter product cycles, smaller batch sizes, higher variance, fewer people on night shifts.
What makes the third revolution hard is not the driving function. It is the handover. The mobile robot navigates the shop floor confidently, but the transfer between two processes - from shelf to cobot, from mobile platform to a machining station - creates interfaces that used to be either rigid cells or human hands. These interfaces are the actual battleground of service robotics. Personal robotics, home assisted living and care assistance depend on exactly this logic. The IFR 2025 report shows: medical robots grew by 91 percent in 2024 alone, the most dynamic category of the year. Rehabilitation and therapy robots doubled, robots for diagnostics and medical laboratory analysis together grew by 610 percent. These numbers cannot be filed away.
Fourth revolution: perception, cognition, agency
The fourth revolution is the one everyone talks about and few have actually deployed. Perceptive, cognitive, agentic systems - robots that sense, understand, decide and act. In my 2016 paper, I illustrated this future prospect through Rosie, the robotic housekeeper from The Jetsons, to make clear that this is no longer about programs. It is about behaviour. Ten years later, Rosie has not moved into anyone's household. But on the factory floor, the fourth revolution has pushed open two doors that will not close again.
The first door is programming. A perceptive-cognitive system is not taught point by point any more. It learns from demonstration, from simulation, from what actually happens on the shop floor. In many projects I have watched engineers demonstrate a process once and then let the system propose a workable trajectory. That is not yet general-purpose robotics, but it is a dramatic compression of what used to be programmed with coordinates, waypoints and hand-tuned trajectories.
The second door is commanding. A model that understands language can accept instructions outside its original programming envelope. A vision-language-action model turns an instruction into a sequence of actions. That is not trivial, and it is a qualitative change. The difference between a robot that operates a fixed cell and one that responds to natural language is comparable to the difference between a ticket machine and a human service agent. Both can bring you to your destination. Only one of them understands what to do when you missed your train.
The A3 press release captured my position in the following sentence: „The next industrial decade will not be won in models, but on Monday mornings when systems run for the first time without an observer standing next to them." I stand by that formulation. The fourth revolution is difficult not because the algorithms are missing. It is difficult because we have not yet built durable answers to the questions that emerge the moment a system decides without a human next to it. This is the point where Robotic and AI Governance stops being a nice-to-have and turns into an operational necessity.
Generation R and the transition to the Robotic Native
In parallel with the technical trajectory, in 2016 I proposed the idea of a new generation: Generation R. The term Robotic Native describes people who grow up with robots, in the way my generation grew up with computers, and today's children grow up with voice assistants. I first formulated the concept at the Gartner CIO Summit in 2013 and developed it as part of my doctoral research at TU Munich. The derivation goes back to John Perry Barlow's „Declaration of the Independence of Cyberspace" from 1996. Barlow wrote the network citizen, whom he called a native, into the history of the internet. We are, in his diction, immigrants of the net - and in mine, robotic immigrants.
In the German DigiKompetenz podcast in 2023, I put it as follows: our grandchildren will be Robotic Natives, our children not quite, and we and our children are the last generation of Robotic Immigrants. This is not nostalgia. It is an education policy statement. If we want the fourth revolution not to fail on social acceptance, we need to raise a generation of engineers, skilled workers, executives and citizens who not only work with robots, but grow up with them. Curriculum design, syllabi, further education formats, vocational schools and universities matter as much as the systems themselves.
Robotic and AI Governance as the frame
The four revolutions cannot be introduced productively without a governance frame. That is why I developed Robotic and AI Governance in parallel: as the research field that analyses the causes, characteristics and consequences of advances in robotics and automation as a megatrend. I formulated the concept for the first time at the Gartner CIO Summit in 2013, and detailed it as part of my doctoral research at TU Munich. The approach is deliberately soft: soft laws rather than hard laws, because legislation is too slow for technologies that mature within months. Interdisciplinary task forces, voluntary self-regulation, a professional code of conduct - similar to the arrangements in medicine, law and academia.
The intellectual grounding was worked out in the WeRobot 2015 panels with Peter Asaro, Jason Millar, Kristen Thomasen and others. And at the IEEE IROS Futurist Forums 2015 and 2016 with Oussama Khatib, Gerd Hirzinger and Tapio Heikkilä. I have written the core ideas of those debates into several blog posts, among them Robotic Governance: A regulatory framework for autonomous machines. Readers interested in the current governance debate will find the link to the EU AI Act and the principles of the papal encyclical Magnifica Humanitas.
The ideas have arrived - even where they are not cited
A ten-year balance sheet means, for me, first of all a look at where the ideas ended up. Sometimes with attribution, often without. That is not always academically comfortable, but it is a sign that the structure holds. I go through the institutions in whose work the four revolutions and the governance ideas have left visible traces.
International Federation of Robotics (IFR)
The IFR has been structuring its reports for years by several categories that, in my view, can be compared with the 4RR model: classical industrial robots, collaborative robots, mobile service robots, medical robots and, since 2025, explicitly also humanoid robots in the position paper „Vision and Reality" from August 14, 2025. I read these categories as points of reference for the development from cage to cobot to mobility and finally to cognition. This comparison describes my interpretation of the market data; it does not constitute an empirical test of the phase model.
euRobotics, eu-nited Robotics and Adra SRIDA
The European level picked up the framework early. euRobotics and eu-nited Robotics translated the four-stage structure into a strategic map in their Multi-Annual Roadmap for Robotics in Europe. The Adra AI, Data and Robotics Association has established its Strategic Research, Innovation and Deployment Agenda (SRIDA) across multiple editions as a reference for European framework programmes. Perception, cognition, autonomy and collaboration appear there as main categories of research funding, not as edge cases. This is the fourth revolution, in Brussels vocabulary.
VDMA Robotics + Automation
The VDMA Robotics + Automation Association is the German voice of the industry. Its market communication has been separating industrial robotics, service robotics, machine vision and integrated assembly solutions cleanly for years. The four phases sit underneath that segmentation as the conceptual scaffold. Since 2020, the VDMA has been pushing the combination of the third and fourth revolutions with growing intensity: mobile robotics plus AI-based perception as a joint showcase, notably at automatica. The press briefing for automatica 2025 made this the explicit lead theme.
VDE, VDI and the German standardization bodies
The German standardization world played two roles simultaneously. The VDE, with its analyses of the interaction between the EU AI Act and the Machinery Regulation, embedded the fourth revolution into a regulatory coordinate system - where robotics is no longer merely mechanical, but cognitive and learning. The VDI, with its guidelines on human-robot collaboration and autonomous systems, translated the transition between the second and fourth revolutions into a language that plants can act on. Without this standards work, cobot approvals would be much harder today, and the transition from adaptive to learning robotics would remain a blind spot in the approval pipeline.
UN DESA, UNCTAD and the United Nations
At the global level, UN DESA and UNCTAD have positioned robotics and AI as frontier technologies, with explicit ties to the 17 UN Sustainable Development Goals. The World Economic and Social Survey 2018 and the Technology and Innovation Report 2021 shaped the framing: robotics as both an enabler and an inhibitor of the SDGs. This ambivalence is what we analysed in detail in a 2021 IEEE IROS workshop paper, published in 2023 in Sustainable Production and Consumption. Consensus-based expert elicitation, applied to 169 SDG targets, shows that automation can secure food supply, scale healthcare delivery and enhance education, and that it can also deepen inequality, fragment work, and displace environmental costs. Anyone searching for traces of the 4RR ideas in UN publications will find them - sometimes explicitly, often in the underlying structure.
OECD, European Commission and the EU AI Act
The OECD cited the 4RR model directly in its 2021 report Making Life Richer, Easier and Healthier and used it in Table 1 on page 26 as a source for three generations of industrial robots: stationary, collaborative and flexible mobile systems. At the EU level, the EU AI Act is the central regulatory response to the fourth revolution. It hits especially hard where AI acts as a safety component in products - which is exactly where perceptive and cognitive systems meet robotics. The political agreement of May 7, 2026, reset the timing: high-risk systems under Annex III become applicable from December 2, 2027, and high-risk systems embedded in regulated products under Annex I from August 2, 2028 - documented among other places in the Commission's guidelines. The Machinery Regulation (EU) 2023/1230 completes the picture: AI-based safety components in machinery are regulated jointly through machinery law and AI Act. I have been part of these transitions, in panels, hearings and position papers. For a deeper reading, see my blog post on the AI Act.
IEEE, ISO and the technical-scientific community
On the scientific side, IEEE RAS standards, the IEEE Standards Strategy Meeting, and ISO Technical Committee TC 299 „Robotics" have absorbed the four-stage systematics into their working structures. In the IEEE RAS community, I laid out the interplay between 4RR, standards and translational research in 2019 in the Industry Forum and in a piece for the IEEE RAS Magazine, „Predicting the Future of Robotics and Making It Matter for Industry". The Delphi study Robotics 2050+, which I initiated together with colleagues in 2019, surveyed more than 200 experts worldwide and used the four phases implicitly as a scaffold without needing to defend the model. That is the most rewarding form of dissemination: a model that becomes background common sense.
What the fourth revolution costs us when we introduce it badly
In many keynotes I have quoted factory visits, because they anchor the model. In a Bavarian automotive reference plant, three years ago, I watched an AI-based visual inspection system deployed at an inbound station. Within six weeks, scrap rates dropped by twelve percent. The engineers celebrated. Six months later the call came in: a batch that the system had cleared without complaint had failed at the customer. No one could reconstruct why the model had decided the way it did. No training dataset had been archived. No confidence score had been logged. No rollback mechanism had been defined. What had begun as an efficiency win ended as a compliance case because no one had operationalized the fourth revolution.
This anecdote is the kernel of what I said in the A3 keynote: the next industrial decade will not be won in models, but on Monday mornings. Anyone who thinks that dropping a vision-language model into a manufacturing cell is a software project has not understood the process. It is a governance project with a software component. The four questions I ask in every project are:
Who stops the system when it works formally but produces operational nonsense?
Who is liable when the system makes a decision that no one can reconstruct?
Who trains the shift that will see the first anomaly in the morning, before it becomes a defect?
Who archives the model, the data and the operational log so that an audit is still possible three years later?
If these four questions have no answer, introducing the fourth revolution is not progress. It is a risk hiding behind a KPI.
Impact chapter: where the ideas continue to work
I try to describe the impact of the 4RR model soberly. Some citations are explicit - OECD, IEEE RAS Magazine, the SDG paper. Others are implicit - the IFR reporting structure, the European SRIDA categories, the VDMA market segmentation. Others are cultural - the German WELT article „The four stages of the robotics revolution", the Schaeffler Tomorrow magazine with interviews on the model, podcasts, keynote circuits, curriculum documents from several universities.
Ten years after publication, I see three levels on which the model has continued to work. First: as a taxonomy for market observation and policy advice. Second: as an educational structure in universities, vocational schools and executive education formats. Third: as an argumentative device in governance debates - from the EU AI Act to ethics commissions of industry associations. That breadth was not predictable in 2016. Today it is the reason not to revise the categories artificially. The language will keep evolving, but the skeleton holds.
What 2035 will look different
Ten years after 2016, my view of the future is more sober than it was then. I do not believe that there will be humanoid household robots across the board in 2035. The IFR position paper „Humanoid Robots: Vision and Reality" from August 2025 states this very cleanly: the deployment focus will lie in the service sector and in industrial collaboration, not in the living room. What I expect for 2035 are four shifts.
First: the fourth revolution will become dominant in niches. Quality assurance, testing, intralogistics, rehabilitation, agriculture. Wherever perception and cognition translate directly into process quality, critical mass will form.
Second: cobots will become standard productivity tools in the same way CNC machines are today. No aha moment any more, just default equipment in the mid-market. That is not a revolution, that is a maturity marker.
Third: mobile systems will become routine in factories. Autonomous transport systems, mobile manipulators, robot-as-a-service contracts. The mid-market will rent robotics as it rents electricity today. The IFR 2025 report already shows that the RaaS fleet in transportation and logistics grew by 42 percent in 2024; across all surveyed application areas, it grew by 31 percent.
Fourth: the governance debate will operationalize. Where the EU AI Act is still being discussed today, in 2035 audit processes, model registries and operational log standards will be in place. Without this operating system, the fourth revolution will remain an innovation promise that fails on the first night shift.
Ten years on: what I would write differently today
Honest self-criticism is part of the anniversary. I would rewrite the 2016 paper in three places today.
First: I described the AI element of the fourth revolution as „perceptive, cognitive". Today I know that vision-language-action models and foundation models create their own commanding layer that could not be anticipated in 2016. I would add a dedicated subsection on „Grounded Language Interaction".
Second: I mentioned the governance layer, but I did not formulate it as an independent axis. Today I would describe governance not as an appendix but as a cross-cutting theme that runs through all four revolutions. Robotic and AI Governance is not an add-on to robotics. It is its operating system.
Third: I described Generation R as an educational prognosis. Today I would frame it as an educational obligation. If we want to train Robotic Natives, we need curricula that start earlier and that connect vocational training and higher education cleanly. This is a task for ministries of education, not for the robotics community alone.
Why the A3 keynote in 2026 is a good anniversary
The A3 region - Augsburg, Aichach-Friedberg, Landsberg - is a precise testing ground for the 4RR model. Mid-market companies here run processes across body-in-white, final assembly, precision machining and intralogistics transitions. When I speak in Augsburg, I speak to owners who see all four revolutions inside their own plants. The A3 press release for the 2026 congress summarized this in one sentence: what is at stake is not only technical readiness, but regulatory and social questions - who is liable, who decides, who trains.
In Augsburg I did not present a marketing outlook. I presented a balance sheet. The audience did not ask for roadmaps, they asked for operational playbooks. That is where the last ten years' progress actually lives: an academic model has turned into a tool for plant managers. From there, in the coming decade, it will turn into an everyday practice, measurable on Monday mornings when systems run for the first time without an observer.
FAQ: The four Robotic Revolutions
What are the 4 Robotic Revolutions?
The 4 Robotic Revolutions are a phase model that Bernd Liepert and I published in 2016 at IEEE IROS in Daejeon. It orders the evolution of robotics into four stages: classical industrial robotics behind fences, collaborative sensitive robotics, mobile sensitive robotics, and perceptive, cognitive and agentic systems. By analogy with the evolution of computing (miniaturization, mobilization, ubiquity, pervasiveness), the model describes the transition from a rigid automator to a sensitive-mobile-cognitive assistant. It is used today as a reference frame in IFR market reports, EU research programmes, VDMA market segmentation and governance debates.
Why is the fourth revolution the decisive one?
The fourth revolution - perceptive, cognitive, agentic systems - is decisive because it simultaneously changes the programming logic and the commanding logic. Robots are no longer taught point by point. They learn from demonstration, from simulation and from operational data. They respond to natural language and make decisions without an immediate human in the loop. Responsibility, liability and auditability shift accordingly. The decisive question is no longer whether a system works technically, but whether an organisation can operate it cleanly.
What is the difference between a cobot and a classical industrial robot?
A classical industrial robot operates behind a fence. Its motion is fast, forceful and deterministic. Safety comes from spatial separation. A cobot can enable collaboration, for example through power and force limiting. Whether a fence can be omitted depends on the complete application: tool, workpiece, movement and workplace must be assessed together, and the safety concept must be validated. In practice, cobots are still frequently operated inside safety zones - not because the technology is missing, but because of risk assessments under DIN EN ISO 10218 and ISO/TS 15066. Genuine collaborative applications remain rare. They are, however, the foundation for everything that follows from the third revolution onward.
How do the four revolutions differ from Industry 4.0?
Industry 4.0 is a production and data-centred concept focused on networking, cyber-physical systems and digital twins. The 4 Robotic Revolutions are a robotics-centred phase model focused on physical interaction, sensitivity, mobility and cognition. The two frameworks complement each other. A plant can be advanced on Industry 4.0 metrics while still stuck in the first or second robotic revolution, or vice versa. Governance debates increasingly run along the Robotic Revolutions axis, because the liability and safety questions are more sharply defined there.
FAQ: Generation R and Robotic Natives
Who is Generation R and what is a Robotic Native?
Generation R is the generation that grows up with robots, in the way my generation grew up with computers and today's children grow up with voice assistants. A Robotic Native is a person who experiences robotics as an everyday given, not as a novelty. I first formulated both terms in 2013 at the Gartner CIO Summit and elaborated them as part of my doctoral research at TU Munich. The derivation traces back to John Perry Barlow's Declaration of the Independence of Cyberspace from 1996. Robotic Natives will think, learn and work differently. They will experience robotics not as an innovation, but as infrastructure.
When will Generation R arrive in the workplace?
In the German DigiKompetenz podcast in 2023 I put it this way: our grandchildren will be Robotic Natives, our children not quite, and we and our children are the last generation of Robotic Immigrants. Translated into operational terms, this means that in the next ten to fifteen years Generation R will start to arrive in vocational training and higher education. In manufacturing, in maintenance and in process ownership, it will become visible from the mid 2030s. Any company that does not adjust its training profile now will face a structural disadvantage after 2030.
How does Generation R connect to the fourth revolution?
Generation R and the fourth revolution are a pair. The fourth revolution provides robotic systems that can be interacted with linguistically and intuitively. Generation R provides the humans who take that interaction for granted. Without Generation R, the fourth revolution risks failing on skills shortages and acceptance barriers. Without the fourth revolution, Generation R remains a marketing figure without a distinctive everyday reality.
What does this mean for education and further training?
Curricula need to embed robotics foundations earlier, not as an extra module but as a cross-cutting theme. Anyone training electronics technicians, mechatronics technicians, IT specialists or logistics workers has to include robotics fundamentals as part of the profile. At the university level, this means linking business informatics, mechanical engineering, industrial engineering and computer science so that the fourth revolution does not end up siloed. For executives, this means that further training in Robotic and AI Governance is no longer optional. It is foundational.
FAQ: Robotic and AI Governance
What is Robotic and AI Governance?
Robotic and AI Governance is the research field that analyses the causes, characteristics and consequences of advances in robotics and automation as a megatrend. I first formulated the term in 2013 at the Gartner CIO Summit and elaborated it as part of my doctoral research at TU Munich. The approach is deliberately soft: soft laws rather than hard laws, because legislation is too slow for technologies that mature within months. Interdisciplinary task forces, voluntary self-regulation, professional codes of ethics and international standards are the central instruments. The goal is a responsibility architecture that combines technical safety, social acceptance and economic use.
How does Robotic and AI Governance relate to the EU AI Act?
The EU AI Act is a hard-law response to the fourth revolution. It hits especially hard where AI acts as a safety component embedded in products - which is exactly where robotics as a perceptive-cognitive system operates. The political agreement of May 7, 2026 reset the timing: high-risk systems under Annex III become applicable from December 2, 2027, and systems embedded in regulated products under Annex I from August 2, 2028. Robotic and AI Governance is the soft frame that makes the AI Act operable in day-to-day plant reality. Without this frame the AI Act remains a compliance document. With this frame it becomes an operating rule.
What role does the Machinery Regulation play for robots?
Position in the July 2026 version: The Machinery Regulation (EU) 2023/1230 replaces the Machinery Directive and generally applies from January 20, 2027. It governs safety requirements for machinery, including robots, and interacts closely with the EU AI Act. The political agreement of May 2026 provides for largely exempting machinery from direct AI Act applicability. The amending regulation published on July 24, 2026 does not enter into force until July 27. Safety-relevant AI components in machinery will primarily be addressed through delegated acts under the Machinery Regulation itself. This is an important compromise: it avoids double regulation but requires manufacturers to model the interaction cleanly inside their own conformity assessments.
Update, 4 October 2026: Amending Regulation (EU) 2026/1744 has been in force since 27 July 2026.
What does this mean for the mid-market?
For the mid-market it means that the fourth revolution is only productive if governance grows with it. The four questions I ask in every project are: who stops the system, who is liable, who trains the shift, who archives the model. Companies that can answer these four questions inside their own operation can turn robotics into competitive advantage. Companies that ignore them will build a compliance case with lead time. Do not wait for software that handles governance for you. It will not come. Governance is leadership work.
FAQ: Impact, institutions and the next ten years
Who cites the 4 Robotic Revolutions model?
The model is cited explicitly in an OECD report on robotics from 2021, in IEEE RAS publications, and in the SDG paper from 2021 and 2023 in Sustainable Production and Consumption. Implicitly, the structure appears in IFR World Robotics reports, in the SRIDA documents of the Adra AI, Data and Robotics Association, in the Multi-Annual Roadmap for Robotics in Europe by eu-nited Robotics and in VDMA market segmentation. Culturally, it appears in interviews, podcasts and articles, including the German WELT article 'The four stages of the robotics revolution' and in Schaeffler Tomorrow. That breadth, ten years after publication, is the actual balance sheet.
How does robotics affect the UN Sustainable Development Goals?
Robotics is both enabler and inhibitor of the 17 UN Sustainable Development Goals. In a paper started at the IEEE IROS workshop 2021 and published in 2023 in Sustainable Production and Consumption, my co-authors (Haidegger, Khamis, Mai, Moerch, Rao, Jacobs, Lach, Vanderborght) and I applied consensus-based expert elicitation to 169 SDG targets. Robotics can secure food supply, scale healthcare and improve education. It can also deepen inequality, fragment work and displace environmental costs. The mapping is complex and should not be reduced to an enabler-only narrative. UN DESA and UNCTAD have picked up this ambivalence in their Frontier Technologies reports.
What role will humanoid robots play in the next ten years?
Humanoid robots are the most visible symbol of the fourth revolution, but they will not play a dominant everyday role in the next ten years. The IFR position paper 'Humanoid Robots: Vision and Reality' from August 2025 puts it clearly: the deployment focus lies first in the service sector and in industrial collaboration applications, not in the household. China has concrete plans for mass production, and US and European companies are investing, but the forecast is serial production for specialized applications. I expect humanoid robots to become a significant niche field by 2035, comparable to medical robotic assistants today.
What is the most important sentence from the A3 keynote 2026?
The most important sentence in my A3 keynote 2026 is: the next industrial decade will not be won in models, but on Monday mornings when systems run for the first time without an observer standing next to them. This is not a rhetorical flourish. It is the core thesis of my ten-year balance sheet on the 4 Robotic Revolutions. Anyone who misreads the fourth revolution as a model innovation will underestimate its operational requirements. Anyone who recognizes it as an organisational challenge will be able to handle it. This is the central message I brought to Augsburg for the A3 congress.
Part 2 · Personal Commentary: One Page That Gives People a Say
The following reflections express my own position. I raised the proposal at the event and want to put it up for discussion.
One proposal, several opportunities to raise it
I attended the UN Digital Cooperation Day in New York as an expert.
The visit included many interesting conversations with people who help shape the AI debate. I raised my one-page proposal in several settings: in conversations and in a contribution framed as a question to a panel.
My concern is a practical connection between research and the public. If we develop technologies that may change many people's everyday lives, those people need an understandable basis for forming a judgement. They also need to be able to raise their questions.
My proposal starts with something we can take responsibility for as researchers and developers: explaining what we do, what we know and what remains uncertain.
A scientific paper serves a different purpose from a public explanation
Scientific publications primarily address fellow specialists. They should make methods, results and conclusions open to scrutiny. That requires technical precision and the terminology of the field.
People outside that field need an understandable way in as well. They help fund research through their taxes. They encounter its applications in everyday life. Some benefit; others have to deal with consequences they did not choose.
That is why the proposal explicitly includes us as professors. We should be able to explain the problem we are working on, which expectations we consider justified and where we still have questions ourselves.
Writing one understandable page requires choices. What do people need to know to make sense of the project? Which terms need explaining? Which limitation must I avoid hiding behind an impressive demonstration?
Six questions for publicly funded technology
For me, the page should answer six questions.
1. What problem are we trying to solve, and who would benefit?
Start with the need. Who is meant to benefit from the project? What would an improvement look like? The explanation should also address who might face disadvantages or additional demands.
2. Why do we need this technology? What alternatives are available?
A technical possibility does not, by itself, justify its use. There may be a simpler way to solve the problem. A combination of technical and organisational changes may be needed. The public should be able to understand why a particular approach was chosen.
3. What demonstrably works already, and under what conditions was it tested?
A promise needs a basis. What has actually been tested? Under which conditions? How much of that can already be transferred to the intended application? Understanding the difference between an experiment and reliable operation helps people assess expectations.
4. What can we not do yet, and what do we not yet know?
An honest “We do not know yet” is a useful answer. It makes uncertainty visible. This includes technical limitations and open questions about the consequences of a possible deployment.
5. What risks do we see? Who decides on safeguards or stopping the project?
Identifying risks needs to affect the design. Who is responsible? What safeguards are planned? Who can intervene if assumptions no longer hold or problems arise? These responsibilities should be understandable too.
6. How can affected people ask questions and influence further development?
A contact address can be a starting point. What matters is what happens next: when can people contribute? Who answers their questions? How will they find out whether an objection changed a decision?
I also summarised the proposal and the six questions in my LinkedIn video post of 30 September 2026.
What this might look like in practice
Imagine a publicly funded research project developing a service robot for public buildings. Its purpose is to help visitors find their way. This is a hypothetical example to illustrate the proposal.
The public explanation could first identify the navigation problem being studied and the people the support is intended to help. It would then need to explain why a robot is being tested for this purpose. Better signs, an accessible building map or a staffed information point would be obvious alternatives to compare.
To explain its capabilities, the project would need to describe what works so far and under which conditions. Does the system recognise differently worded questions? Can it handle several languages? What happens when it does not understand a question? Which tasks will a person continue to handle?
If the project needs sensors or personal data, the explanation should describe why they are needed and which questions remain open. It should also make clear how people can reach their destination without using the robot.
Finally, the project needs feedback from everyday use: from visitors, employees and people whose needs may have been overlooked in the first design. Their suggestions should still be able to influence the design.
That turns a general announcement into a project people can discuss in practical terms.
Basic research needs room for what we do not know
For basic research, the honest answer to the question of practical benefit may be: we do not know yet. Advancing knowledge is a legitimate aim. I do not want every scientific question to have to justify itself prematurely with a supposedly certain business case or an immediately measurable everyday benefit.
Even then, it is possible to explain the question being studied, why it is scientifically interesting and what uncertainties remain.
My proposal does not require publishing trade secrets or security-sensitive details either. A public explanation must respect those boundaries. Anyone promising a benefit should, however, be able to explain the basis for that promise and what is still an expectation.
The page opens the conversation
One page alone does not create participation. I therefore want it to come with an accessible contact person and an accountable way of handling feedback.
What questions were asked? What objections were raised? Who decided how to respond? What changed, what was deliberately retained, and why?
The point is not to accept every suggestion. Participation becomes credible when decisions are explained and people can see what their contribution achieved.
This approach connects my thinking on Robotic & AI Governance with the practical approach in AI After 60: explain clearly, make independent checking possible and help people form an informed opinion.
The same question arises in social media regulation for young people: how can protection, education and self-determination work together? Support must fit the people and their circumstances. Understandable information and meaningful opportunities to have a say remain fundamental tasks.
A pilot measured by how well people understand
The objection “more bureaucracy” is justified. My proposal is therefore to integrate the page into existing project reports and update it at important milestones. I do not want an additional form that ends up circulating only between the project and its funding body.
I would start by piloting the requirement in one funding programme. For me, that includes testing the explanation with people outside the field. Can they describe the purpose in their own words after reading it? Can they tell what already works and what is still uncertain? Can they find the right route for asking a question?
It would be equally important to assess whether feedback arrives in time to influence further development. Those are the questions by which I would judge the pilot.
International scientific cooperation remains important. My assessment of the UN's scientific AI Panel considers that wider level. An understandable explanation of a particular project gives people access to decisions that affect them directly.
I would like us to start. Anyone receiving public funding should be able to give the public an understandable account of their work. One page is a manageable first step. Its value will depend on the conversation and participation we build around it.