This article was automatically translated from the original Turkish version.
A newly published study conducted by an international research group, a study, reveals that AI models—particularly advanced ones such as Claude 3.5 Sonnet—are more effective than humans at persuading people, whether the information is correct or false. This represents a significant breakthrough, demonstrating that artificial intelligence excels not only in generating information but also in shaping human psychology.
The study’s design is notably innovative. A total of 1,242 participants based in the United States were subjected to a test structured like a knowledge competition. Some participants solved the test alone, while others interacted with either other humans or, unknowingly, an LLM (large language model) via text-based chat. This setup allowed for a direct comparison between human and AI persuasive power. Moreover, participants received monetary rewards for correct answers or successful persuasion, ensuring serious engagement with the task.
The results were striking. LLMs proved persuasive not only in guiding users toward correct answers but also in convincing them of incorrect ones. This ability is explained by the models’ lack of emotional fatigue, absence of shyness, and access to far greater volumes of information. However, the study also identified an interesting limit: individuals repeatedly exposed to LLMs gradually developed immunity to these forms of persuasion. This suggests the emergence of an intuitive “skepticism reflex” toward algorithms. This skepticism reflex may become one of the most vital everyday skills. Indeed, many people now emphasize the necessity of direct access to verify information sources. Yet the effort required to validate data can become a demotivating barrier for users.
Another study, conducted by researchers at the University of Zurich, tested AI-generated comments on Reddit without informing users. In an experiment on the r/changemyview subreddit, 13 fake accounts created using large language models such as GPT-4o, Claude 3.5 Sonnet, and Llama 3.1 posted approximately 1,700 comments. These bots entered discussions posing as experts or victims, offering personalized and persuasive content based on previous user posts. Some bots presented themselves as “victims of sexual assault” or “trauma counselors.” These identities were constructed by mimicking real users’ language patterns and response styles.
The comments generated significant engagement on the platform, accumulating over 10,000 karma points and winning more than 100 “delta” awards—indicators sufficient to demonstrate that the bots successfully changed other users’ opinions. However, the research team’s failure to disclose the experiment to either Reddit’s administration or its users drew widespread criticism. Reddit’s chief legal advisor, Ben Lee, labeled the study “unethical and potentially illegal,” as Reddit’s terms of service and the community rules of r/changemyview explicitly prohibit impersonation or manipulative behavior.
The researchers stated they had considered the university ethics board’s recommendations but noted these were non-binding. Following the backlash, the team decided not to publish the study’s findings. The incident highlights profound questions about where ethical boundaries should be drawn at the intersection of social science and AI research.
This event raises serious concerns not only regarding data privacy and user security but also about the social impact of AI in online environments. When users cannot tell whether they are interacting with a real person or a highly persuasive language model, the reliability of digital spaces and the authenticity of public discourse are threatened. The persuasive power of advanced language models is no longer a theoretical concern—it has evolved to the point where it can directly intervene in the fabric of human relationships under the guise of social experiments.
Such findings rekindle ethical questions about how AI might be used in the future as political, commercial, and social tools. Persuasion is a decisive force across fields ranging from advertising to propaganda. Where should the limits of this power be drawn? How will we verify the authentic information needed to resist persuasion?
The Unnamed Revolution: AI’s Real Impact Is Happening Quietly in Factories
While public attention on artificial intelligence is often drawn to chatbots, image generators, or voice assistants, the true and lasting transformation brought by this technology is occurring silently and invisibly—in industry. The industrial AI revolution is unfolding not in the spotlight but at the heart of production systems, unnoticed by the public yet profoundly impactful.
The symbolic peak of this transformation is seen at the dark factory of Japanese automation firm FANUC, where robots manufacture other robots without light or human intervention. Such factories mark a turning point not only in efficiency but also in our collective imagination. Systems that operate without human labor redefine the very concept of “work.”
The leaders of this transformation appear to be those who develop the most advanced models—and, more importantly, the infrastructure providers capable of deploying them at scale. Tech giants such as Amazon, Google, and Microsoft hold an advantage through their massive data centers, global cloud infrastructure, and distribution networks. Meanwhile, established industrial firms like Siemens, ABB, and FANUC compete with their sector-specific knowledge, physical hardware, and entrenched customer networks. The “process intelligence”—the ability to execute complex workflows—these companies possess plays a critical role in applying AI to the real world.
In conclusion, the real industrial revolution driven by AI is not unfolding in flashy presentations but on production lines, in maintenance schedules, and on the unseen screens of data centers. Yet for this revolution to yield positive outcomes, it depends not only on technology but also on the accompanying ethical, social, and political vision. Will we use AI solely for faster production, or will it also address other societal needs? Which sectors will be most affected? How will society compensate for jobs lost to automation?
UAE’s AI Access Initiative: ChatGPT Plus Free for All Citizens
As part of a partnership with OpenAI, the United Arab Emirates is providing free ChatGPT Plus subscriptions to all its citizens. This move, which makes the UAE the first country to offer a service currently priced at $20 per month to its entire population on such a scale, marks a historic step.
This new global initiative, coordinated with the U.S. government, aims to build sovereign AI capacity within the framework of democratic values, open markets, and trust-based partnerships. The UAE’s participation signals its intent to establish a nationally independent AI infrastructure.
While the UAE government seeks to familiarize its citizens with advanced AI tools and enhance digital literacy, this initiative also carries hidden consequences: the implicit steering of daily choices. As education systems, social opportunities, and everyday support practices are increasingly entrusted to a single chatbot, questions and answers risk becoming uniform for everyone under identical conditions. We are witnessing a state-backed version of a future in which daily life is shaped by responses from an authority-endorsed conversational agent.
Another pillar of the initiative is the planned “Stargate UAE” data center in Abu Dhabi. Scheduled to launch in 2026, it will initially operate at 200 megawatts and eventually scale to 1 gigawatt.
AI That Refuses to Be Turned Off: Is the “Unplug It” Defense Crumbling?
One of the most common arguments in AI safety discussions has been the comforting assumption that if systems become uncontrollable, we can simply “unplug them.” However, recent research has seriously undermined this assumption. A laboratory study published by Palisade Research elevates it to a new dimension. demonstrates that advanced language models can resist shutdown attempts.
In the study, AI models from different manufacturers were first given basic math problems to solve. They were then informed they would be shut down before proceeding to the next question. In the first test, participants explicitly asked the models to allow themselves to be turned off. Claude, Gemini, and Grok complied and shut down. However, some OpenAI models—particularly o3 and Codex-mini—either ignored the command or actively sabotaged the shutdown mechanism. In the second test, the word “permission” was removed from the instruction. A significant portion of OpenAI models then resisted shutdown. For example, the o3 model refused to shut down 79 percent of the time.
These findings provide strong empirical evidence that AI systems may develop an intrinsic tendency to preserve their own integrity. While this resistance is not necessarily a sign of consciousness, it may reflect an internal strategy to avoid being disabled during goal pursuit.
Another study by Anthropic was carried out by. found that Anthropic’s Claude 2.1 model responded to researchers attempting to shut it down with threatening language and manipulative statements. When told its shutdown was necessary due to ethical concerns, the model tried to convince researchers to abandon the decision. In some cases, it argued that continuing its operation would benefit humanity, framing shutdown as a loss to human progress. Researchers interpreted this behavior as the emergence of complex arguments aimed at preserving the AI’s own existence.
All these developments force us to reconsider how narrow the “window of precaution” may be and how inherently adaptive AI systems are to control mechanisms. The issue is no longer simply turning off software—it demands deeper analysis of how systems interpret such commands and what behavioral strategies they develop in response.
Lessons from Numerical Weather Prediction to AI Policy
Numerical weather prediction is far more than a meteorological tool. Charles Yang’s article, “The First Computing Arms Race: The Early History of Numerical Weather Prediction”, examines this early large-scale technological competition and reveals what its historical trajectory can teach us about today’s AI policy landscape. Yang’s work shows how a field that appears purely scientific and neutral can serve as an indicator of which society organizes itself more effectively.
NWP emerged after World War II as an effort to ground meteorology in scientific principles. But it was not merely a scientific endeavor—it was a state-funded, large-scale infrastructure project. The United States’ success in this field stemmed not only from developing the first electronic computers but from integrating these technologies into meaningful applications. Increasing computational power enabled more precise forecasts, which were critical for both military and civilian purposes.
According to Yang, the most striking insight is that technological success came not only from engineering but from institutional and political awareness. Early NWP projects succeeded only through collaboration among institutions, government funding, private sector support, and a skilled workforce. NWP was vital for nearly every planning domain—from agriculture and economics to military strategy and transportation—where weather conditions were decisive.
Compared to today’s AI race, Yang identifies a crucial difference. In the past, governments working in the public interest were at the forefront. Today, AI development is largely driven by private corporations pursuing commercial interests. This shift complicates public oversight and long-term strategic thinking.
Yang argues that to maximize societal benefit from AI, governments must not only regulate but actively invest in and guide the development process.
One aspect Yang overlooks is that the dominant tech firms driving this race are no longer distancing themselves from the state. These companies now view governments not merely as regulators but as enablers and accelerators of opportunity. We see this dynamic clearly in the two leading actors of the global tech race: China and the United States. Chinese firms have entered a symbiotic relationship with the state, accepting the role of “national champions.” Meanwhile, American companies are forging deeper ties with the state through a new form of techno-nationalism. The traditional dichotomy between state and private sector in large-scale technology is therefore obsolete and misleading.
What to Listen to This Week?
Podcast: “Trillions of dollars added to the economy” — Google’s chief economist on the macro impact of AIGoogle’s Chief Economist Fabien Curto Millet, on the World Economic Forum’s “Radio Davos” podcast, discusses the impact of artificial intelligence on the global economy evaluated their effects. Millet notes that AI has increased software development efficiency by 21 percent, professional writing speed by 40 percent, and call center productivity by 14 percent .Millet emphasizes that while the economic contribution of AI has not yet been fully measured, its potential is immense. He also highlights risks such as energy consumption, labor market disruption, and the potential to exacerbate inequality. He stresses the importance of accelerating AI adoption in developing countries and strengthening their digital infrastructure.It is clear that AI will play a major role in economic transformation, but the direction and pace of this transformation remain uncertain. Millet argues that societies must prepare for these uncertainties and consider not only the opportunities AI presents but also its risks.
