Category: AI

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Sustainability in computing

As the huge size of data centres become visible for everybody we begin to question the sustainability of computing infrastructures as well. The need for energy and water resources (for cooling) grow in line with the growth of data centers, the whole issue of input of resources and global trade has to be re-assessed. Based on European trade data, we know that computer software is heavily imported from the US, whereas we import the hardware to run the software in even higher shares from China. In view of the AI boom, this risks to worsen the European trade balance in the coming years. European digital sovereignty suffers as well, if we do not act upon it (compare Figure 8 in Eurostat report).
An easy fix is the shift to more computing-efficient software, which does not need or rely on more processor and memory imports from China for the hardware and imports of services like operating systems or office software from the US.
Just changing to Linux and OpenOffice lets you keep your hardware for several more years. Sustainability in computing isn’t hard to do. It is just a matter of determination. We can do it, if we really want to do it. The more rare earths become rare and more expensive, the larger the amount of people and businesses, who shall think twice about this.
European Digital sovereignty can work as a driver of sustainability in computing as well.

rainbow in front of clouds Brussels Central 2025

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Socio-technological obsolescence

The standard literature or AI-sytems will give you a definition of on technological obsolescence, which specifies that obsolescence does not mean that a device is broken, but that it is outdated. In computers this might be due to hardware no longer supporting newer, more resource demanding software, or newer software insisting on the use of other hardware. The seemingly rapid innovation cycles in smartphones, cars or robots might justify such technological obsolescence, but the real advances like shifts from 3G to 4G to the newer 5G mobile frequency standards have taken place rather slowly due to provider coverage of sufficiently large, particularly rural areas.
Therefore, the technological obsolescence has to be enlarged as a concept to socio-technological obsolescence as the societal, legal and economic boundaries of technological innovations have to be taken into account as well. Provisions for health concerns or CO2 saving circularity, i.e. reuse of resources have to be taken into account as part of a precautionary principle.
Computer screens have asked us to move from square designs to wide screens (watch videos) to smartphones’ standards of long formats. My 20 years old square screen has been doing a reasonable job throughout these periods, though not for serious games.
The socio-technological obsolescence relies on a “socio-technical prestige score” of products, like for luxury brands in other industries, where fashions drive obsolescence more than technology.
(Image: Robotic arm made by Kuka writes on paper sheet at Frankfurt book fair 2017)

 

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Deep Fake Threat

Our Western democracies are aware that “deep fake videos”, radio, online-newspapers and most of all social media platforms are all around us already. However, more scientific voices alert us that this threat to our easy or comfortable way of life to consume information eventually threatens the survival of our democracies. Previously, interference in elections used to focus on rigged election procedures, but in the 21st century powerful other alternatives can do the dirty job to bias elections against the original intentions of the electorate. The widespread use of AI will exacerbate the already practiced ways to produce deep fakes. In a preparatory self-test of an AI-assisted chatbot I was surprised myself of the quality of the output. A person not very familiar with my original voice in a second language would assume that it is me who is being interviewed in person. Based on a fake news text, any form will be automatically translated into voice only and/or video based on basic visuals.
Statisticians used to joke some decades ago: “Don’t believe in any statistics, unless you faked it yourself.” This is meant to encourage people to be aware of dangers of the use of statistics to influence opinions or official decision-making, like in policy making of central banks, which might be based on biased accounting for shrinkflation, cheatflation or greedflation to name just a few,
Hence, the need to strengthen awareness, analytical skills and critical thinking should be high on the agenda to defend our democracies. There are not only external military threats, but additional ones masked as internal threats.
(Image:: mice as humans in living room 2 couch potatos 1 on rocking chair, tea time)

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Robotics Hype 2026

Towards the end of 2025, it is common practice to look back on the last 12 months to summarize a year and to contribute to the “collective memory” of the year. From a “society and technology” perspective we shall not be surprised if such summaries will be full of images and praise of AI and robotics. However, large parts of the innovations that shall be declared to have marked 2025 were already around 10 years ago. It is just the timing for the new momentum and the creation of a hype around these technologies that is really remarkable (compare WSJ 2025-11-24 p 1-2 by Konrad Putzier).
It is true, playing around with robotics was reserved to universities, research institutes and some big players in industry. The public and financial markets showed little interest in these “nerdy” fields of applications. Although we were hardly able to compete with our chess computers, Watson solving math problems for us including the steps for us to follow. Video, image and textual support was provided by specialized applications already at high levels and in multilingual versions. In 2025 these techniques have enhanced with machine learning and neural network programming reaching higher speed and being able to use ever larger data sets as input.
But there are areas where the hype is coming to an end. How about all the artificial reality (AR), virtual reality (VR) applications? Many have seized to exist. Have you visited or invested in “Second Life” platforms? Opened a shop in the VR-world? Bitcoins have lost 7% of their value between 1.1.2025 and 24.11.2025 and they suffer still from high volatility rather than an uninterrupted rise.
War has fuelled the rise of shares in 2025 and “dual-use” technology benefits as well. AI has been driven by, and drives both trends.
In sum, it is much less the technological innovations in 2025 that are astonishing, but the political economy of how to orchestrate a sensational hype around the technologies.
(Image Hannover Industry Fair 2016-3-14).

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Sovereign data spaces

Data is the new gold, petrol or diamonds. In order to bring this message home to all people in the EU, the European summit on digital sovereignty had a small exhibition of projects that address these issues. City data spaces is such an initiative which has been running for quite some time now. In fact, from a city planner and data scientist perspective cities collect already huge amounts of data and can offer them to service providers, businesses and each and every one of us to organize our energy consumption, improve mobility patterns or any form of data or video streaming services. The amount of data captured and to be stored is growing rapidly. Just think of the Internet of things (IoT), maybe that’s only your wifi-connected coffee machine, oven or heating. Now add AI to this which allows the system to learn about your daily patterns to start the device in time for you to focus on other tasks. As we would like these data to stay confidential, the need for European digital sovereignty becomes sufficiently clear. It will take a huge effort to provide an adequate digital infrastructure for this “brave new world” and many people to work towards this objective. Train the trainers already, cause otherwise this is going to take ages before we can harvest the benefits in safe and sovereign manner.  

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Reverse causality

Reverse causality is a beast, which empirically minded scientist fear almost like death. However, many processes we study are running not only in one direction. In most cases, causality is tested with, or assuming, a unidirectional model of causality in mind. But some processes have not only a set of multiple causes to take into consideration, but some processes might be reversible or run in a rather complex manner, which are difficult to quantify. Mind captioning is a technique in neuroscience, where easy language is used to describe an image perceived in a person’s mind. Such thinking aloud data is based on thousands of brain scans, where people watched videos or images (study link).
In my own journey into the working of my mind I play around with different directions of causality. Sometimes the text is the origin and the image follows in a selection of a telling illustrations, but occasionally the reverse causality is at work. The image is the starting point and gets the mental process going. It is a rather complex process which is not easy to approximate with the help of algorithmic thinking. Reverse causality has many surprises to offer. As scientists we have a hard time to come to grips with it. (Inspiration Link) 

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EU Digital Sovereignty

If we try to search for digital solutions, we shall encounter a whole lot of American and Chinese products, but very few European companies that are able or willing to compete. Hardware mainly comes from China, software from the US, at least until AI was not working in the background. If we add Russian interference to destabilize our digital infrastructure to the scenario, we are not really fit for the challenges of the 21st century. The very definition of a country or political union is the affirmation and competence to assure its sovereignty, particularly in cases of territorial conflicts with neighboring countries. My health or mobility data are a rather private affair, however, our state governments in EU-Europe have done little to ensure our data integrity. Business is also at a loss, if they do not spend heavily on data security themselves, usually relying on external cooperation. 

The EU digital sovereignty summit took place in Berlin on the EUREF campus in 2025. It can only constitute a beginning for intensified cooperation in  this long overlooked policy area. It will be tough to catch up where production has been abandoned for decades.  

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Typewriter history

The history of the typewriter and typewriter is comparatively short compared to the history of literature or other technologies as partners in the creative process. With the advent of AI (here as part of infografix, see image below) the skills of using and mastering a typewriter have become almost obsolete. The original design by Remington (timeline below) has dominated for almost 100 years the technology of typewriters. Then came the electronic IBM technique with an automated correction type, which was not only faster, but also more forgiving of “typos”, short for typing errors.
The craft of handwriting had suffered a tough blow, despite its almost intimate touch to it. Knowing the typewriter outline by heart allowed typing with closed eyes or a focus on another text or image as well as a parallel thought process. Scientists and writers (Claude Levi-Strauss) reported on their creative process as intrinsically being linked to their typewriter.
QWERTY outlines for English language typewriters still dominate the keyboard typing today. With the AI interaction on the rise, we might move away from typing as a “Kulturtechnik” a technology of our cultural era and focus more on human-machine interactions via our voice and microphones. The underlying question, however, remains the same: What is the best technology to enhance our thought process? This, in fact, tends to be a very personal human choice, where technology plays only a subsidiary role.

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From AI to xAI

As humans, we like the feeling to be in control of things. This applies even to immaterial things like religious beliefs. Generative AI has created problems with its hidden structures and lack of transparency of their applications of algorithms (and combinations of algorithms) to basic data bases of knowledge and information. The use of xAI, which stands for explainable artificial intelligence, can address some of the concerns about the lack of transparency and explanation of responses from AI systems. Many users want to know in advance about the consequences of the use of specific words or notions in an instruction to AI. The interpretation of each single word by xAI can inform about the precision of interpretation (cheap versus cheapest, for example) or highlight the sensitivity to gender-neutral language or not in its guidelines. Additionally, ex post the xAI could indicate alternative notions in a prompt and, briefly, how this would affect results.
Yes, there is a trade-off between brevity of answer and room for explanations. As in psychology, there some value in a “thinking aloud” procedure for respondents in order to better understand (implicit) the reasoning behind a reply. xAI takes us a step further in this direction of asking AI to think aloud or more explicitly in a human compatible way of logic and broader reasoning.
Put AI on the psychotherapist’s bench and xAI will be to the advantage of many more humans again. Humans just don’t like black box systems that lack the necessary as well as sufficient transparency. (Image on the right: Patrick Jouin, chaise solide C2, MAD digital humanism).

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AI as individualizer

In a one pager in the journal “Rolling Stone” (2025, p. 9) Bruno Patino writes about the legendary David Bowie who was the first rock musician to launch a new song on the internet before it became available as CD (Telling Lies, 1996). As a pioneer in co-creation, Bowie anticipated somehow the trend and wish of people to personalize preferred songs even further and distribute such versions among friends. In this process, AI has become a powerful tool to push individualization even further and the digital social media allow even broader audiences beyond a more narrow circle of friends. Music maybe setting the trend  for some in the same field, other creative fields might follow the footsteps. The need to co-create collective experiences and participate in collective musical moments is likely to rise again as well.
Good news for music festivals across the world. Live concerts are the new form “collective individualism”.

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Passing barriers

In quantum physics the real trick is not the rebound of electrons like the rebound of droplets, but the passing of electrons of an insulating barrier. The experiment of the ”Josephson junction” has set a precedent to research the surprising macro-effect of “quantum tunnelling”. The 2025 Nobel Prize has been awarded to Clarke, Devoret and Martinis who observed these effects on a macroscopic scale. The applications in the most advanced quantum computers of today shows the enormous potential of this demonstration in pushing computing speed boundaries. The international competition to develop such, ever faster computers, based on quantum physics, is running on high development speed. In combination with the artificial intelligence (AI) developments, these types of combined machines are likely to outpace the development of human-based intelligence. It becomes even more important to define the limits for those machines by us. Subsequently, we shall have to make sure that such combined machines stick to the rules, we define(d).

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Deus ex machina

The term “deus ex machina” used to be applied more in its figurative meaning. With the rise of digital tools like chatbots, facilitated and enhanced through AI, God is speaking to us not only in multiple languages, but also from our pockets through our smartphones and headsets. This is a rather recent form of “deus ex machina”, which we did not expect some years ago. The bible as e-book or pdf-file has been around for some decades, but only more recently we can enter conversations with God through chatbots as another version of “deus ex machina “ about almost everything (and pay for it via digital credit card). Programming of such an AI-tool is easily achieved. AI will prepare a weekly or daily sermon or prayer for you, following your predilections of your favourite quotes of the bible. An interesting twist to the programming is to use authorized as well as unauthorized translations of the bible across several centuries.
Another interesting enlargement of the input data base is the inclusion of interpretations and discussions not only within your own religious community, but beyond. Maybe the discussion of several different religious chatbots with each other could prevent aggressions due to differences in basic beliefs. These “dei ex machina” might further our understanding of what makes us humans different from machines and machine-based solutions of human conflicts.
As genetic clones of ourselves have become already technically more feasible, our digital alter-egos (the comprehensive collection of traces in the internet and digital images, plus social scoring) help to empower those “dei ex machina”.
This kind of “Brave New World” asks us to be rather brave ourselves.
(Image: interior St Denis Basilique Cathedral Paris 2024)

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Chatbot Me We

In order to dig deeper into the functioning of AI, I deemed it expedient to construct, for example, a simple chatbot on a limited knowledge base from my own writings on AI (link to reader in previous blog entry here).
A toolbox from Google offers powerful assistance in such an endeavour. The outcome uses only my input text and no other sources. It is dynamic in the sense that it interprets questions and searches within the text file provided only. The answers are edited with a LLM (large language model) and provide flawless English texts. You can try it here using catchat as magic formula and Google account so far.
With a bit of programming knowledge (htlm, python, Java) and related learning sites it is feasible to come up with a “static” chatbot hosted at a free of charge provider as well. For learning purposes this step by step building and coding of a chatbot is helpful. The outcome is rather limited or requires a lot of time to increase the scope of Q & A interactions and to move from a static (predefined Q & As) to dynamic ones.
Full control of answers, excluding any hallucinations and high-speed replies, come at a cost. Take a look here. It is a very basic version so far, just to get the idea of it. full web address:
https://schoemannchatbot.eu.pythonanywhere.com/

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Chatbot Me

Chatbots are helpful to allow queries to larger data sets like the blog entries here. So here is a try of a Chatbot to query all entries on AI using ChatGPT to create a Chatbot that uses and references it source from www.schoemann.org/tag/ai and the AI reader in pdf-format.
Please send me an email if the hallucinations of this Chatbot 1.0 on AI from a social science perspective are giving strange results. I’ll get back to you. Please use at your own risk as I cannot guarantee for all answers. The usual disclaimer applies here.

ChatGPT proposed the following set of Questions and Answers on the blog for an entry into the chat: Example Q&A with the chatbot

Q: What are the social science concerns with AI?
A: Bias in results, job shifts, democracy risks, privacy, and new inequalities.

Q: What does the text say about reinforcement learning?
A: It’s seen as the next step for AI: focusing on learning and reasoning, not just predicting text. It also uses fewer resources.

Q: How are robots described in the document?
A: Robots are mostly assistants. They can follow people or carry small items, but more complex tasks need sensors and AI training.

Q: What about biased results?
A: Studies can be misleading if control groups are flawed. AI faces the same challenge — social scientists warn: “handle with care”.

Q: What is Schoemann’s blog view on AI?
A: He links AI to energy use, fairness, and its role in the “all-electric society” — stressing efficiency and responsibility.

More on the chatbot (in testing phase) and the Link to the coding help received from ChatGPT on this mini-test-project :
https://chatgpt.com/share/68c1d160-0cc0-8003-bf04-991b9e7c3b24

 

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AI Podcasting Me

Content producers have lots of tools at their disposal to get their content across to very different audiences. For some time the traditional media of newspapers, radio and TV were the prime outlets for content distribution. Social media have changed this to many more senders of content than before.
In the 21st century, AI allows to automate media productions. In a trial run I just used Google’s NetbookLM to generate 3 podcasts based on my own writings on AI over more than a year by now. The result is available and using artificial voices it is possible to broadcast yourself without revealing your own personal voice. I am not done with the evaluation of the outcome(s) yet, but the first impression is an interesting other form to spread content.
More tests are necessary to check for hallucinations as well.
Here are the links to my virtual podcasts:
AI, intimacy and insecurity

AI, Society and the Human Spirit

AI and the Human Mosaic: Navigating Our Interconnected Future

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Video Doku by AI

Based on my own blog on this webpage “schoemann.org” Google NotebookLM creates a video of about 7 minutes. Using Microsoft Clipchamp automatic subtitles with a slightly different storyline are produced based on the video data. In the end, the blog entries are re-modelled into something like a lecture on “AI in a wider social context” (see and play below). No voice layover so far, read by yourselves. A podcast format is another option.
It feels like walking across landscapes in my own mind. Content creators of today or the past never imagined the impact they might have through the powerful tools of AI. The only caveat, jokes I incorporated into the texts cannot really be handled by AI tools unless they are explicitly designated as such. These AI tools take me much more seriously as I do myself. This is serious.

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Mind Map Me

AI tools are great to assist learners in the task to get more structure into larger documents or books. It is up to the teachers or lecturers to use the tools themselves to pre-structure content they want other persons to learn. Mind maps are useful to summarise larger content and offer a tree-like structure to a text moving from the general to more specific content and then into details by at the same time not loosing sight of the overall structure of the content. Basics can be provided by Google’s NotebookLM and you may rework this basic structure yourself linking the mind map to the detailed content. Learning may start with a comprehensive mind map at the beginning to move on to details. Alternative versions of a mind map are equally feasible to come up with new combinations of subjects. This can be done using the tags of the blog entries in addition to the categories and fast search keywords.
It is a fascinating way to mind map yourself based on longer texts written by yourself. This clarifies a bit what potential readers or learners are likely to retain from reading your document(s) or blog entries.
Its worth trying, as others will most likely use these tools from now onwards anyway, whether we like it or not.
Image: Google NotebookLM Mind Map of my AI blog entries 2025-9-9.

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Own AI related entries

The digital drafting of blog posts facilitates the publishing of volumes based on a selection of entries. Pdf-files, or EPUB formats are quite common. The choice of a flipbook is more suitable for larger screens of tablets or notebooks. It also serves as a rapid stock taking of entries and missing topics to cover a broader field. In some fields AI is not yet popular, for example in agentic home applications, whereas in other fields text, translation or image production AI has grown into a threat to professional fields. Even computer languages are a major field of application with substantial negative employment effects.
Hence, enjoy the latest comments on AI towards the end of my latest Flipbook.
Just click here.

ChatGPT provides a summary of my AI-reader as follows:

From the uploaded text AI from a Social Science Perspective (Dr. Klaus Schoemann), social science concerns with AI focus on potentials and limitations of how AI interacts with society:

  • Labour and employment: AI impacts jobs, but adjustment processes are slow. For example, there’s a shortage of engineers and care workers, while robots are still limited to simple assistant tasks. This raises concerns about who benefits and who is left behind.
  • Bias and fairness: The book highlights issues of biased results in research and decision-making. When AI systems rely on incomplete or selective samples, results can be misleading — a challenge also familiar to social scientists.
  • Democracy and accountability: Journalists and scientists are framed as part of a “checks and balances” system. AI adds complexity here, as the fast spread of information without accountability can weaken democratic processes.
  • Human–machine relations: Topics like intimacy, housing, and memory are re-thought in the context of AI and digitalization, raising questions about privacy, social norms, and cultural heritage.
  • War and security: AI-driven technologies like drones change the nature of warfare, raising ethical and political questions.

On his blog, Schoemann also stresses the energy and resource costs of large AI models and how reinforcement learning offers a more efficient alternative. He warns that AI should be understood not just technically, but as part of an “all-electric society” where social sciences help assess risks, inequalities, and long-term consequences.

👉 In short: AI raises social science concerns about equity, bias, democracy, labour, cultural shifts, and global risks.

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Marc Aurel AI

In the 21st century it is possible to chat with Marcus Aurelius. Part of the exhibition at the Simeonstift is a chatbot you may freely consult and questions with or about Marc Aurel. Based on your questions the animated screen image of Marc Aurel will reply based on his own writings like the Meditations and (probably) other secondary literature on Marc Aurel. Questions about feminism or slavery are answered based on the original texts. Some of these answers  appeared rather modern like the basic equality of all including women or slaves. The Meditations are an idealistic vision of mankind in the stoic tradition. In practice such ideals have proven very ambitious for the many and growing temptations in the day-to-day lives of ordinary people including their political, religious, business and military leaders. The AI is confronted with the issue to give answers to ethical questions which refer to the time of the author, but not all can apply to today’s ethical standards and basic human rights. Reading the original source, therefore, remains the preferred choice. 

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AI in Central Banks

Yes of course, Central Banks will use AI, and some do so already (Kazinnik and Brynjolfsson, 2025). Beyond the standard application of AI by its employees, there are many potentials to use AI to analyse and publish data at a faster rate or in order to detect financial crimes. Similarly, data collection based on webpage harvesting might yield new indicators of inflation, expenditure for environmental risks (heat waves, flooding etc.) earlier and in addition to the normal set of indicators. Hence, Central banks might be better and faster in forecasting inflationary tendencies using more AI tools in their daily routines. Of course, it is difficult to predict a disruptive tariffs policy of a major economic player in the world economy, but the calculation of more, even hallucinatory scenarios become more feasible. It is feasible to weigh overall risks of different scenarios to the economy.
(Image: Celtic coins, Museum of the Belgian National Bank)

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AI earnings effects

In the first few years of wider adoption of AI in an economy, there is the expectation that this might lead to substantial productivity gains for enterprises which use it as well as for employees who are early adopters of the relatively new technology. The study by the Stanford Digital Economy Lab by  Chen, Chandar and Brynjolfsson (2025) showed that so far there are no significant earnings effects for employees. Based on millions of recent payroll data from US companies productivity gains have not trickled through to the paycheck in terms of monthly salaries. Participation of staff in a company’s overall turnover or profit might change this as time evolves. For civil servants the adoption of AI might mean increases in cases dealt with as some tasks can be executes faster than before with the use of AI.
The evidence points to employment effects of AI rather than earnings effects so far. A hypothesis is yet unresolved: senior employees using AI might employ fewer junior workers at entry positions, if these “hallucinating” young professionals can be replaced by hallucinating AI. In science the hallucination has sometimes lead to disruptive new approaches and findings. It is a tough choice to pick the young entrants with high productivity potential and eventually high remuneration for this in terms of labor earnings.

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AI employment effects

The first robust empirical evidence about employment effects of AI in the USA has been published by the Stanford Digital Economy Lab by  Chen, Chandar and Brynjolfsson (2025). A previous paper by Wang and Wang (2025) highlighted the comparative advantage of persons who use AI in their work compared to others and the authors coined the term “learning by using technology”.  The prediction of the model was that there might be job losses of more than 20% in the long run and half of this already in the first 5 years of the introduction of the technology. The Stanford economists have estimated with real world data these effects in the USA and find quite surprisingly that the negative employment effects of AI have the strongest impact on young labor market entrants with few years of labor market experience. Middle-aged and more senior employees seem to benefit from “tacit knowledge” about the work, which is more difficult to replace with AI, at least for the time being of the early days of AI. This evidence is based on recent payroll data from the largest payroll processing firm “ADP” in the USA which has firms overrepresented from the manufacturing and services industries as reported in another paper  (Firm size maybe another source of bias).  However, the effect that youth 22-25 years of age suffered the most calls into question the common belief that older workers are more likely to suffer the consequences as during in the rise of the digital economy around the year 2000. (AI Image created with Canva)

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Bench the benchmarks

In the social sciences as well as in engineering it is common practice to use benchmarks as indicators of performance. Thereby, several countries or regions within a country are compared with respect to quantitative indicator. Let’s take employment ratios. A higher employment ratio, which includes many persons working few hours in part-time work, is different from a slightly lower employment ratio, but hardly any part-time employees.
The same rationale holds true for benchmarks of AI systems or the newer versions of agentic AI that are under construction in many fields. The paper by Yuxuan Zhu et al. (2025) proposes the ABC (agentic behavior checklist) for agentic AI developers. The reporting of benchmarks by such models should include (1) transparency and validity, (2) Mitigation efforts of limitations and (3) result interpretation using statistical significance measures and interpretation guidelines.
The aim of this research is to establish a good practice in establishing benchmarks in the field of agentic AI. The sets of criteria to test for is large and the focus of how the agentic AI treats, for example, statistical outliers much above or below the average i.e. (> 2 standard deviations from the average) assuming a normal distribution, is one case of application only.
We welcome the efforts to bench the benchmarks in the field of AI as is common practice in other sciences as well.

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Learning by using

Is learning by using different from learning by doing? In an economic model to test the employment/unemployment impact of AI in the USA, Wang & Wong (2025) suggest an important impact of employees’ productivity due to learning by using AI. In terms of the traditional language of economics the employees who use AI in their work shall have comparative advantage to those who don’t.
In a model of job search in the economy there is the additional possibility, similarly to robots previously, that certain tasks maybe influenced by the, more or less, plausible threat of an employer to replace the employee by training an AI system to perform the tasks. The credibility and acceptability of such threats are likely to impact wage claims and unemployment risks. All these effects do not happen instantaneously, but evolve over time with varying speed. Hence, calculations of effects have high error margins. The resulting model yields oscillations of “labor productivity, wages and unemployment with multiple steady states in the long run”.
Learning by using seems to be a good description of what occurs at the micro level (the employee) and at the macro level of an economic sector or the economy as a whole. Society may guide the use cases of AI just as much as the business case to use AI, for example in the creative industries as infringements of copyrights may occur on a massive scale. However, learning by using is not free of risks to society at large. Just like allowing people to use automotive vehicles has lead and still leads to thousands of deaths annually, learning by using produces external costs. Overall, this is another case for a benefit/cost analysis for businesses, the economy and society.

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AI 2nd round effects

The most popular topic currently is AI.
Most writers, assisted by some form of AI, will deal with the 1st round effects of AI. These consist in the immediate consequence of the use of AI in office work, medical and military applications, music and all producing or creative industries. As an economist you take the input – output matrix of the economy (OECD countries) and take AI as an additional dimension of this I/O matrix, for example. The result is an AI-augmented model of the economy. This 3-dimensional cubic view of the economy asks to reflect on the potential short-term and medium-term impact of AI.
Let’s take the example of translation and editing services. AI will in the short-term or the 1st round effects make it easier to offer mechanical translations with fast turnaround. Most likely, this will lead to less translators needed for routine translations of longer texts, which would otherwise be a very costly endeavour. The 2nd round effects, however, will make the expert knowledge of translators of texts, where every word counts, more necessary in order to provide the best version of a translation targeted on specific audiences.
In the legal domain, for example, the precision of words is primordial and errors can be very costly. Hence, the 2nd round effects of AI in this field will increase the demand for high quality translation services more than before the use of AI. The important shift consists in these 2nd round effects of AI, which give a push to multilingual societies as just one medium-term outcome.
Please use AI to read (listen) to this paragraph in your native language or even dialect using your favourite AI-tool.

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Home Leaks

When did you last think about leaks in your home?
Usually we associate leaks with water leaks, or the heating system leaking somewhere. In the 21st century leaks at home are more importantly the leaks of your home security, especially your email, digital and cloud services which are at risk. You may test your favourite AI system to advice you on your risks for digital leaks, but we know little whether these systems are yet another dangerous port of entry into your home or privacy themselves.
There is a helpful tool to find, whether your email has been hacked or distributed widely already for potential thefts of your identity. Hence, better check this from time to time using for example the “leak checker“, just like checking whether you closed your door or the water tap before leaving for vacations. As we live more and more in “virtual homes” in addition to our physical homes, checking your digital identities should become a part of our personal hygiene routine. Let’s just take a shower from time to time and change passwords regularly.

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Hallucinations serious

There serious hallucinations by AI and there are funny hallucinations by AI. Do we want our various AI models, from time to time, to crack a serious or funny joke? Well, that’s a bit the spice of life. However, not knowing when the machine is joking and when it is serious, this is more likely to seriously disturb most of us. This reminds us of our school days were teachers were not amused some pupils not taking them seriously in their efforts to transmit information. Now we know that a good atmosphere is conducive for better learning progress. AI as teaching and learning assistance could well work best in a “fearless“ classroom. Repeating a lesson several times and at your own learning rhythm will help independent of the seriousness of your teacher. Self-directed learning with a little help by AI might do the trick for many to advance how and when they feel ready for it. Hallucinations rates are a standard test for AI models. They range from 1% to 25% of queries.  This is not in itself a problem. It has become tough to find out about the 1% -2% models because you no longer expect them to give wrong information. These are the 1-2 out of a hundred of cases where we are confronted with serious hallucinations, seriously.
(Image: extract from „cum Polaroids“ from Eva & Adele, Hamburger Bahnhof, Berlin 2024-5-22)

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Home security

Digitalization has made it possible to step up home security at reasonable costs. The video surveillance of homes inside and the immediate surroundings are feasible through the use of connected cameras. The footage can reach sizable amounts of data, but intermittent recordings reduce or the AI-assisted detection of movements on a person’s property have become standard home security. Even for apartments the video enabled door bells and digital locks have improved the security level for those who are willing to invest in home security. A good neighborhood watch system is, of course, in most cases a superior solution. But neighbors change and social interaction is often reduced to minimal contact in most suburban regions. As with heating of homes, home security is also depending on what makes you feel comfortable at home. For some 20 degrees Celsius is enough and a solid mechanical lock is sufficient. Others have made very different experiences and want their digital devices directly linked to a professional security or police service. Like it or not, home security is part of the modern home just like many other digital devices or TV sets. 

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testing testing

Before the installation of the new AI chatbots or other agentic AI, they need profound testing. Wise statistics are quoted with the conviction: it is all about testing, testing, testing. Any systems that build on statistical reasoning (LLMs or machine learning) will behave erratically on what is known as an area with stronger impacts of, for example, statistical outliers. On both ends of the “normal distribution” of events or reasoning the statistical models and algorithms used in AI will produce “spurious” errors or have larger error margins on such topics a bit off the 95% of usual cases.
This means, testing, testing and testing again for the programmers of such AI systems before the release to the public or enterprise specific solutions. The tendency to keep costs of testing phases low  compared to developing costs bears obvious risks to the “precautionary principle” applied in the European Union. Testing is most important to check the WEIRD bias of the most basic AI systems. In this sense AI development has become a sociological exercise as they have to deal with “selection bias” of many kinds that could have very expensive legal consequences.
(Image: Extract from Bassano, Jacopo: Abduction of Europa by Zeus, Odessa Museum treasures at exhibition in Berlin Gemäldegalerie 2025-5).

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Humanoid services

In the shadow of AI enhanced chatbots, agentic AI and generative Ai, the developers make considerable progress in robotics. The humanoid versions like from Persona AI will surround us in months, or maybe a few years from today. Investors believe it is rather sooner than later. There are many use cases for humanoids that may take over dangerous, hazardous or unhealthy tasks from humans. But even simple tasks like carrying home most of our shopping could be done for us by humanoids that follow you around the shopping mall and home. This would be a kind of personal assistant. I even thought of my humanoid robot to walk my dog on some occasions on the usual trail. 

Welcoming visitors at the doorstep could be another function to delegate in offices or even in private homes, although as a sociologist I would recommend to carefully check the sorting algorithm(s) applied to avoid unpleasant situations. The administration of medication might be another option, if only we could trust that the correct dosage would be applied.