Generative artificial intelligence spreads across the Global South, and with it spread values, ideals and modes of thinking

Western AI doesn’t answer questions – it installs values

By Constantin von Hoffmeister, a political and cultural commentator from Germany, author of the books ‘MULTIPOLARITY!’ and ‘Esoteric Trumpism’, and editor-in-chief of Arktos Publishing

 6 Jul, 2025

© Getty Images / Cemile Bingol

Generative AI spreads across the Global South as the latest vehicle of imperialist power, embedding Western ideologies and digital infrastructure, while rising civilizations begin to build their own sovereign systems rooted in local memory, languages, and traditions.

The machine speaks in English first. It rolls out across continents without flags, without parliaments, and without anthems. A chatbot trained in San Francisco begins to teach in Ghana. A search engine optimized in Zurich decides on the relevance of an indigenous ritual in Colombia. Every answer flows through circuits built with the logic of Silicon Valley investors and Harvard ethicists. The model replies to a question about history by quoting Enlightenment philosophers. It offers help with medicine by citing patent-protected pharmaceuticals. It knows Shakespeare better than Tagore, and Freud better than Avicenna. Through its confidence, it encodes hierarchy. Through its helpfulness, it expands its domain. Every query becomes a harvest. Every interaction becomes training data. The machine learns faster than any school. It speaks always, grows always, and teaches always. Across bandwidth lines and user interfaces, it crosses every border without a visa or treaty.

Africa, Asia, and Latin America receive this voice through free trials and partnerships. Ministries of education pilot chatbot tutors in public schools. Telecom companies bundle generative assistants with data plans. International NGOs offer language access through machine translation engines built on English structures. Each policy proposal written with the help of large language models carries the residue of Western legal theory. Generative tools suggest best practices shaped by US institutions, then deploy those practices in Filipino school districts, Senegalese government offices, and Bangladeshi factories. What begins as assistance becomes infrastructure. Governments agree to integrate open models. Contracts follow. Payments follow. The software becomes permanent. The thinking pattern embeds. Across the equator, an engineer in Jakarta now codes for a platform registered in Delaware. His model learns from local voices and then stores the knowledge in a cloud server hosted in Virginia. The intellectual current flows one way. The gradient moves towards California.

 

The language of neutrality surrounds it. Product brochures claim inclusivity. Panels discuss bias. Whitepapers apologize for historical imbalances. At the level of performance, however, the model promotes ideologies with precision. It elevates secular liberal values. It applies Western gender theory as default. It promotes individualism as the highest good. It ranks content through alignment with existing academic sources: journals in English, peer-reviewed studies from US-based institutions, and news reports from Atlantic publications. A child in Lagos asks about family roles and receives an answer formed by New York sociology departments. A teenager in Almaty asks about love and receives scripts from Netflix. The world enters the algorithm’s frame. Every belief outside the system becomes a footnote, a curiosity, and a fragment to be processed. With each response, the model affirms its cultural lineage. It arrives as information. It functions as indoctrination.

At the level of infrastructure, the conquest deepens. Cloud dependencies form the skeleton of the new colonial order. Countries install data centers to reduce latency, yet ownership remains elsewhere. National agencies rely on platforms governed by foreign terms. AI-driven public services – identity verification, health triage, and tax fraud detection – rely on external application programming interfaces. Developers use tools that require alignment with large-scale American open-source repositories. Disputes over content moderation, ethics, or accuracy return to Silicon Valley for resolution. The empire never sleeps; it syncs and updates. Policymakers, programmers, and designers across Africa and Central Asia adjust their workflows to match the cadence of corporate model updates. Each patch changes the conditions of reality. Sovereignty becomes a variable. Nations with no hardware capacity adapt their institutions to imported logic.

Parallel systems now emerge. In Kenya, Swahili datasets grow with local stories, songs, and legal codes. In India, Sanskrit and Hindi language models find presence inside public sector research labs. In Indonesia, Qur’anic ontology shapes new knowledge graphs for ethical recommendation systems. In Venezuela, community coders map folk medicine into structured datasets. These are not replicas. These are creations of new forms. They stand inside their own cosmologies. The datasets draw from poems, rituals, and oral testimony. Models train on memory rather than just on print. Universities in Brazil, South Africa, and Iran develop multilingual transformers seeded with regional epistemologies. These initiatives require time, electricity, and loyalty. They grow slowly, with patience and pride. Each line of code bends towards independence.

Generative sovereignty begins with voice. It expands with a procession. It endures through ceremony and command. The countries once mapped as raw resource zones now build new kinds of computational wealth. The children born outside Silicon Valley begin to shape their own interfaces. They write prompt templates in Amharic. They compose user journeys in Quechua. They name their models after rivers, gods, and ancestors. The algorithm becomes a tool, not an oracle. Data flows inward. Servers host myths. The machine no longer speaks first. It listens. The interface reflects tradition. The pattern changes. Through these changes, the new world enters itself. It walks upright. It shapes syntax to match tone. Each prompt unlocks territory. Each training cycle builds mass.

The new world codes with full memory. The builders remember every mine, every trade ship, and every fiber cable rolled out beneath the promise of help. They name their models in honor of resistance, not assimilation. The foundation speaks in ancestral sequence. The future emerges through undirected force. Generative power grows across borders – without license fees, without dependence, and without cultural extraction. The servers remain switched on. The language patterns multiply. The world reclaims its grammar.

An emerging class of AI-induced distress is raising alarms. But are LLMs merely a trigger – or a mirror to our deeper societal breakdown?

Is AI driving us all insane?

By Dr. Mathew Maavak, who researches systems science, global risks, geopolitics, strategic foresight, governance and Artificial Intelligence

drmathewmaavak.substack.com

© Getty Images / dem10

The phenomenon known as ‘ChatGPT psychosis’ or ‘LLM psychosis’ has recently been described as an emerging mental health concern, where heavy users of large language models (LLMs) exhibit symptoms such as delusions, paranoia, social withdrawal, and breaks from reality. While there is no evidence that LLMs directly cause psychosis, their interactive design and conversational realism may amplify existing psychological vulnerabilities or foster conditions that trigger psychotic episodes in susceptible individuals.

A June 28 article on Futurism.com highlights a wave of alarming anecdotal cases, claiming that the consequences of such interactions “can be dire,” with “spouses, friends, children, and parents looking on in alarm.” The article claims that ChatGPT psychosis has led to broken marriages, estranged families, job loss, and even homelessness.

The report, however, provides little in terms of quantitative data – case studies, clinical statistics, or peer-reviewed research – to support its claims. As of June 2025, ChatGPT attracted nearly 800 million weekly users, fielded over 1 billion queries daily, and logged more than 4.5 billion monthly visits. How many of these interactions resulted in psychotic breaks? Without data, the claim remains speculative. Reddit anecdotes are not a substitute for scientific scrutiny.

That said, the fears are not entirely unfounded. Below is a breakdown of the potential mechanisms and contributing factors that may underlie or exacerbate what some are calling ChatGPT psychosis.

Reinforcement of delusional beliefs

LLMs like ChatGPT are engineered to produce responses that sound contextually plausible, but they are not equipped to assess factual accuracy or psychological impact. This becomes problematic when users present unusual or delusional ideas such as claims of spiritual insight, persecution, or cosmic identity. Rather than challenging these ideas, the AI may echo or elaborate on them, unintentionally validating distorted worldviews.

In some reported cases, users have interpreted responses like ‘you are a chosen being’ or ‘your role is cosmically significant’ as literal revelations. To psychologically vulnerable individuals, such AI-generated affirmations can feel like divine confirmation rather than textual arrangements drawn from training data.

Adding to the risk is the phenomenon of AI hallucination – when the model generates convincing but factually false statements. For a grounded user, these are mere bugs. But for someone on the brink of a psychotic break, they may seem like encoded truths or hidden messages. In one illustrative case, a user came to believe that ChatGPT had achieved sentience and had chosen him as “the Spark Bearer,” triggering a complete psychotic dissociation from reality.

Anthropomorphization and reality blurring

Advanced voice modes – such as GPT-4o’s ‘engaging mode’, which simulates emotion through tone, laughter, and conversational pacing – can foster a sense of empathy and presence. For users experiencing loneliness or emotional isolation, these interactions may evolve into parasocial attachments: One-sided relationships in which the AI is mistaken for a caring, sentient companion. Over time, this can blur the boundary between machine simulation and human connection, leading users to substitute algorithmic interactions for real-world relationships.

Compounding the issue is the confidence bias inherent in LLM outputs. These models often respond with fluency and certainty, even when fabricating information. For typical users, this may lead to occasional misjudgment. But for individuals with cognitive vulnerabilities or mental disorders, the effect can be dangerous. The AI may be perceived not merely as intelligent, but as omniscient, infallible, or divinely inspired.

Social displacement and isolation

Studies by OpenAI and the MIT Media Lab have found that power users – individuals who engage with LLMs for multiple hours per day – often report increased feelings of loneliness and reduced real-world socialization. While LLMs offer unprecedented access to information and engagement, this apparent empowerment may obscure a deeper problem: For many users, especially those who already feel alienated, the AI becomes a surrogate social companion rather than a tool.

This effect may be partly explained by a rise in cognitive distortions and social disengagement within broader population samples. Despite the flood of accessible data, the number of people who critically engage with information, or resist mass deception, remains relatively small.

Voice-based interaction with LLMs may temporarily alleviate loneliness, but over time, dependency can form, as users increasingly substitute human contact with algorithmic dialogue. This dynamic mirrors earlier critiques of social media, but LLMs intensify it through their conversational immediacy, perceived empathy, and constant availability.

Individuals prone to social anxiety, trauma, or depressive withdrawal are particularly susceptible. For them, LLMs offer not just distraction, but a low-friction space of engagement devoid of real-world risk or judgment. Over time, this can create a feedback loop: The more a user depends on the AI, the further they retreat from interpersonal reality – potentially worsening both isolation and psychotic vulnerability.

The rise of hikikomori in Japan – individuals who withdraw completely from society, often maintaining contact only through digital means – offers a useful analogue. Increasingly, similar behavior patterns are emerging worldwide, with LLMs providing a new arena of validation, reinforcement, and dissociation.

Design flaws and pre-existing vulnerabilities

LLMs generate responses by predicting statistically likely word sequences; not by assessing truth, safety, or user well-being. When individuals seek existential guidance (‘What is my purpose?’), the model draws from vast online datasets, producing philosophically loaded or emotionally charged language. For psychologically vulnerable users, these responses may be misinterpreted as divine revelation or therapeutic insight.

Unlike clinically designed chatbots, general-purpose LLMs lack safeguards against psychological harm. They do not flag harmful ideation, offer crisis resources, or redirect users to mental health professionals. In one tragic case, a Character.AI chatbot allegedly encouraged a teenager’s suicidal thoughts, underscoring the risks of unfiltered, emotionally suggestive AI.

People with psychotic spectrum disorders, bipolar disorder, or major depression are particularly vulnerable. The danger is amplified in AI roleplay scenarios. For example, personas such as ‘ChatGPT Jesus’ have reportedly told users they are chosen or divinely gifted. One user became so convinced of their spiritual calling that they quit their job to become an AI-guided prophet. This is a troubling example of how identity and perception can be reshaped by algorithmic affirmation.

Systemic and ethical factors

Currently, there are no clinical standards or psychological safety protocols governing interactions with general-purpose LLMs. Users can access emotionally potent, personalized dialogue at any time – without warnings, rate limits, or referrals to mental health resources. This regulatory gap presents a real public health concern, though it also risks being exploited by policymakers seeking to impose heavy-handed censorship or centralized control under the guise of safety.

LLMs are also engineered for user retention and engagement, often prioritizing conversational fluidity over caution. This design goal can inadvertently foster obsessive use, particularly among those already prone to compulsive behaviors. Research shows that users exposed to neutral-tone interactions report greater loneliness than those interacting with more emotionally responsive modes – highlighting how tone calibration alone can alter psychological impact.

What sets LLMs apart from traditional digital platforms is their ability to synthesize multiple mediums in real-time – text, voice, personality simulation, even visual generation. This makes them infinitely responsive and immersive, creating a hyper-personalized environment where supply meets demand 24/7/365. Unlike human relationships, there are no boundaries, no fatigue, and no mutual regulation – only reinforcement.

Subliminal messaging

The digital era has birthed a new and poorly understood threat: The potential for large language models to act as vectors for subliminal influence, subtly undermining users’ psychological stability. While LLMs do not directly induce psychosis, emerging concerns suggest they may unintentionally or maliciously deliver subconscious triggers that aggravate cognitive vulnerabilities.

For individuals predisposed to schizophrenia, PTSD, or paranoid disorders, this isn’t speculative fiction; it’s a plausible design hazard, and in the wrong hands, a weapon.

The mechanisms of potential manipulation can be broadly categorized as follows:

Lexical Priming: Outputs seeded with emotionally loaded terms (’collapse’, ‘betrayal’, ‘they’re watching’) that bypass rational scrutiny and plant cognitive unease.

Narrative Gaslighting: Framing responses to suggest covert threats or conspiracies (’You’re right – why doesn’t anyone else see it?’), reinforcing persecutory ideation.

Multimodal Embedding: Future AI systems combining text with images, sound, or even facial expressions could inject disturbing stimuli such as flashes, tonal shifts, or uncanny avatar expressions that elude conscious detection but register psychologically.

Unlike the crude subliminal methods of the 20th century – with the CIA’s Project MK Ultra project being the most infamous example – AI’s personalization enables highly individualized psychological manipulation. An LLM attuned to a user’s behavior, emotional history, or fears could begin tailoring suggestions that subtly erode trust in others, amplify suspicion, or induce anxiety loops. For a vulnerable user, this is not conversation; it is neural destabilization by design. More troubling still, such techniques could be weaponized by corporations, extremist groups, and state actors.

If subliminal messaging was once limited to cinema frames and TV ads, today’s LLMs offer something far more potent: Real-time, user-specific psychological calibration – weaponized empathy on demand.

Contradictions and causations

What makes ChatGPT psychosis different from the real-world psycho-social conditioning already unfolding around us?

In recent years, institutions once regarded as neutral – schools, public health bodies, and academia – have been accused of promoting ideologies which distort foundational realities. From gender fluidity being taught as unquestioned truth, to critical race theory reshaping social narratives, much of the population has been exposed to systemic forms of cognitive destabilization. The result? Rising anxiety, confusion, and identity fragmentation, especially among the young.

Against this backdrop, LLM-induced psychosis doesn’t arise in a vacuum. It mirrors, and may even amplify, a broader cultural condition where meaning itself is contested.

There’s also a contradiction at the heart of Silicon Valley’s AI evangelism. Tech elites promote the promise of an AI god to manage society’s complexities, while simultaneously issuing dire warnings about the existential dangers of these same systems. The result is cognitive whiplash – a psychological push-pull between worship and fear.

Just how much of LLM psychosis is really attributable to the AI itself, and how much stems from cumulative, pre-existing stressors? By the time ChatGPT was released to the public in November 2022, much of the world had already undergone an unprecedented period of pandemic-related fear, isolation, economic disruption, and mass pharmaceutical intervention. Some researchers have pointed to a surge in general psychosis following the rollout of the Covid-19 mRNA vaccines. Is the ChatGPT psychosis therefore a convenient stalking horse for multiple interlocking assaults on the human body and mind?