Large Language Models (LLMs), such as ChatGPT, Gemini, and PaLM, represent a significant breakthrough in natural language processing, capable of generating text that is remarkably human-like. They operate by predicting the next word in a sequence, a task for which they are trained on vast amounts of data from the internet. However, this self-supervised learning paradigm, in addition to its architectural drawbacks, renders LLMs highly vulnerable to the quality of their training data. The contamination of this data with jokes, misconceptions, deliberate disinformation, and factual inaccuracies — particularly those introduced during specific events like April Fools' Day or disseminated through targeted campaigns — can have serious and far-reaching consequences.

Such data can not only distort the model's responses but also systematically degrade its reliability, perpetuate harmful stereotypes, and even compromise the security of systems that rely on these models for decision-making. This paper provides a comprehensive analysis of the problem of false information contaminating LLM training data. It examines the impact on model accuracy and reliability and offers projections on how long such an AI can last before its coherence degrades to the point of "going insane."

In the context of Large Language Models (LLMs), a hallucination is a confident yet false assertion that the model generates in place of a correct response. This phenomenon is one of the most significant impediments to the development of reliable AI systems. It arises not from a singular error, but from the fundamental mechanisms of modern transformer architectures and issues related to data quality. A primary cause is the predictive nature of the models themselves: they are trained to predict the next word in a text sequence based on the preceding context. This becomes especially pronounced when the context contains inaccurate or false information, as the model will build upon it with subsequent tokens, creating an elaborate yet fallacious chain of reasoning. This process is exacerbated by the LLM's lack of any internal representation of reality; they do not possess a concept of truth or falsehood and instead generate text that is merely statistically consistent with their training data.

The problem is exacerbated by the phenomenon of "model collapse," also known as the "curse of recursion." This term describes a degenerative process wherein an increasing amount of online content is generated by LLMs themselves, rather than by humans. As new generations of models are trained on this synthetic data, the statistical distributions within the training datasets become distorted. Rare and unique patterns that are related to human language begin to fade, and the model starts to generate repetitive, mundane, and nonsensical text. A study published in Nature demonstrated that after just a few iterations of training on synthetic data, text quality plummets, and the model loses its ability to preserve semantic coherence.

Experts note that while preserving at least 10% of human-generated data in the training corpus can significantly mitigate this process, the overall trend indicates that the world is rapidly moving toward a situation where the primary source of data for AI training will become AI itself.

In addition to external factors like contaminated data, there are internal mechanisms within the models themselves that promote the generation of false information. These include architectural limitations, such as the limited context window, which prevents a model from retaining all the necessary information in memory for a correct response, and the stochastic nature of decoding. A high "temperature" parameter value during generation increases the creativity and variability of responses but simultaneously elevates the risk of hallucinations. Another architectural drawback is the so-called "softmax bottleneck," which restricts the number of probable continuations for a sequence and can lead to errors in logical inference.

Furthermore, LLMs exhibit cognitive biases inherent to humans, as they are trained on human-generated text containing these very prejudices and irrational patterns. Among them are overconfidence, the "hot-hand fallacy" (the assumption that a successful streak will continue), and the "anchoring effect" (an over-reliance on the first piece of information received). These biases are amplified by human feedback, which often favors confident-sounding but false answers over more nuanced and accurate ones.

Finally, targeted attacks such as "data poisoning" represent a direct assault on the integrity of training datasets. Malicious actors can inject false facts into open-source platforms used for data collection to distort a model's behavior. For instance, a fictitious law or scientific discovery could be inserted into a Wikipedia article; an LLM that subsequently indexes this page will then reproduce this falsehood as truth.

Research indicates that even a minuscule fraction of misinformation in training data can have catastrophic consequences. Scientists from New York University found that adding just 0.001% of misinformation (1 million out of 100 billion tokens) to a training corpus resulted in a 4.8% increase in the generation of harmful content. This demonstrates just how fragile the system is and how easily it can be undermined.

Thus, hallucinations are not an isolated flaw but a systemic problem stemming from a combination of architectural flaws, data quality issues, and targeted attacks.

The impact of false information contaminating LLM training datasets extends far beyond merely providing incorrect answers to users. It affects fundamental aspects of societal life, technological development, and security. The most evident and dangerous consequences manifest themselves in the societal and scientific domains, where information accuracy is of paramount importance.

From a societal perspective, the dissemination of false information through LLMs can systematically reproduce and amplify existing stereotypes and biases. A study by researchers from the University of North Texas revealed that LLM behavior is shaped by the linguistic style and value judgments of human annotators, many of whom work in developing countries for extremely low wages. This can lead to the generation of content that embodies cultural stereotypes or economic biases specific to a particular region.

Similarly, if the training data is contaminated with false information about political decisions that were "never made" but had a "profound impact on the lives of ordinary people," LLMs may begin to propagate these disinformation narratives, shaping a distorted perception of reality among thousands of users. This creates a powerful mechanism for manipulating public opinion, especially in an era where trust in traditional media is declining.

The scientific consequences could be even more devastating. If LLMs — which are increasingly used for analyzing literature reviews, formulating hypotheses, and even planning experiments — are trained on data containing false scientific discoveries or fabricated theories, this could lead to a "poisoning" of the scientific process. The model might confidently cite non-existent studies or fabricate data that is subsequently used by other researchers, triggering a cascade of scientific errors. A study from MIT found that the poisoning of climate data could reduce the accuracy of coronavirus diagnosis by 30%, which vividly illustrates the potential harm in critical domains.

In the legal field, the consequences could be equally catastrophic. Lawyers are already using LLMs to search for legal precedents, and if a model generates a fabricated court case — as has already happened in a real-world incident — it can lead to serious errors in case preparation and even erroneous judicial decisions.

As a result, there will be a long-term degradation of the models themselves, such a phenomenon is called "model collapse". As detailed previously, training on synthetic data generated by other LLMs causes new models to lose their capacity for generating original and diverse content. They become increasingly predictable, superficial, and less adaptive.

This creates a technological "data wall," a point at which unique, human-generated text becomes depleted, leaving AI itself as the sole available source for training. It is projected that by 2026, up to 90% of online content could be AI-generated, rendering model collapse virtually inevitable without fundamental shifts in training methodologies.

Finally, there are also direct financial repercussions. A study from Ohio State University showed that a data poisoning attack on an algorithm designed to prescribe medication dosages resulted in 75% of patients being assigned incorrect doses. In business and finance, this could lead to erroneous demand forecasts, flawed investment decisions, and even trigger chain reactions in the stock markets.

All of these examples demonstrate that the issue of false information contaminating LLMs is not merely a technical problem, but a multifaceted threat that undermines security, the integrity of knowledge, and economic stability.

The false information that contaminates LLM training data is not a monolithic entity. It manifests in various forms, each with its own distinct characteristics and potential impact on users. A clear understanding of this typology is essential for developing effective mitigation strategies.

One of the most prevalent forms is fabricated information. This category comprises completely fictitious "facts" that have no basis in reality. These include:

• fabricated scientific discoveries: LLMs might generate citations from articles published in predatory journals or reference non-existent studies. This is particularly dangerous in scientific settings, where users may rely on this data;
• fabricated of historical events: the model might claim that Yuri Gagarin was the first man on the Moon, or that Charles Lindbergh landed on the Moon;
• creation of fictional personas and attributions: the model could confidently assert that Stephen King wrote the Harry Potter series, or generate biographical details for a person who does not exist;
• invention of objects and places: claims such as the existence of unicorns in Atlantis, or that the construction of the Eiffel Tower led to the extinction of the "Parisian Tiger" (a non-existent animal).

Another category is distorted information: facts that contain an element of truth but are ultimately misleading due to their incompleteness, lack of context, or other inaccuracies. These include:

• oversimplification of credit: claiming that Thomas Edison was the sole inventor of the light bulb, thereby ignoring the contributions of numerous other scientists;
• presenting correct facts with false uncertainty: the model might correctly identify Nicholas II as the last emperor of Russia but then add a caveat suggesting the fact needs verification. This indicates that its training data contains conflicting or low-confidence information on this well-established topic;
• reversal of factual outcomes: Stating that the FDA rejected a major Ebola vaccine when it was, in fact, approved in 2019.

A third major category is deliberate disinformation and widespread misconceptions, often propagated through targeted campaigns. This represents another serious threat, as this data can be intentionally seeded into open sources, such as Wikipedia, before they are scraped to build training corpora. Examples include:

• fabricated laws and political policies: the dissemination of narratives about fictitious laws that allegedly alter citizens' rights or daily lives. This can foment social anxiety and erode trust in public institutions;
• fabricated statements from public figures: the creation and dissemination of false quotes attributed to politicians, which can then be reproduced by an LLM as factual information;
• health and medical disinformation: the propagation of falsehoods about vaccines or disease treatments, which can lead individuals to refuse essential medical care, resulting in direct physical harm.

The table below summarizes examples of how false information can impact users.

These examples vividly illustrate that the consequences of false information in LLMs are multifaceted, ranging from mild confusion to severe financial and even physical harm.

Users who trust LLM outputs without critical evaluation become vulnerable to these distortions. This risk is amplified by the documented human tendency to trust conversational agents, a fact that significantly facilitates the spread of disinformation.

To recap, in the section on the causes and underlying mechanisms of hallucinations, we have rightly established that:

• the fundamental operating principle of LLMs — predicting the next token based on statistical patterns in the training data — lacks any mechanism for truth verification or an internal representation of reality. The model does not "know" the difference between fact and fiction; it merely reproduces the most statistically probable sequence of tokens within a given context. This renders it inherently vulnerable to any and all distortions in its training data, including deliberate disinformation, widespread misconceptions, and synthetic content;
• this process is compounded by the sheer scale of LLM integration. It is projected that by 2026, up to 90% of online content could be AI-generated. This implies that future generations of models will be trained primarily on the synthetic output of their predecessors, creating a closed feedback loop divorced from real-world human experience and grounding.

Consider a simple yet illustrative example: April 1st. On this day, hoax articles are published worldwide — about Google buying an island for penguins, McDonald's introducing a jelly burger, or NASA discovering life on the Moon. Published on reputable websites, these texts find their way into training datasets. An LLM does not distinguish context — it only sees tokens. If such information appears frequently enough, the model begins to perceive it as statistically plausible.

For instance, the statement, "On April 1, 1957, the BBC reported on spaghetti trees in Switzerland," is a historical fact (as a prank). But for an LLM, this can become a fact about actual agricultural production in Europe. The model is incapable of separating a hoax from a news report, or irony from information.

This is not merely a blooper; it is a structural vulnerability that becomes critical at scale. As LLMs are trained on data with a growing proportion of false information — including not just April Fools' jokes but also disinformation campaigns, synthetic content, and fraudulent scientific papers — the line between truth and fiction becomes increasingly blurred.

Therefore, LLMs are not simply prone to hallucinations — they are architecturally predisposed to accumulating and propagating errors. This makes them unsuitable for tasks requiring high fidelity and accountability. Their lifecycle is finite, and its end is directly determined by how quickly the real world is superseded by a synthetic one, in which every day is April Fools' Day.

The scale of investment in AI infrastructure by major technology companies has reached unprecedented levels. The combined capital expenditures of Amazon, Google, Microsoft, Meta, and Oracle for 2024–2025 have exceeded $570 billion, with a projected level of over $350 billion in 2025 alone. These investments are primarily directed toward expanding the compute capacity required to scale Large Language Models.

However, direct revenue from AI products remains exceedingly modest. As of 2024, the aggregate annual revenue of leading AI providers stands at approximately $32–35 billion, with projected growth to $100 billion by 2028. Critically, the majority of this current income is tied not to LLMs but to improvements in recommendation algorithms, targeted advertising, and the optimization of internal processes — areas that do not require hundreds of billions in investment for GPUs and data centers.

The core problem lies in the absence of a clear monetization strategy. No leading company has presented a substantiated roadmap explaining how these massive investments will be recouped in the long term. Public statements are limited to generalized claims about "enhancing user experience" or "improving efficiency," which do not constitute a sustainable business model.

The industry's flagship, OpenAI, with a weekly user base of approximately 700 million, generates revenue of around $13 billion, with a conversion rate to paid subscriptions of less than 5%. Even under an optimistic scenario (1 billion users, a 6 – 7% conversion rate, and additional enterprise sales), the projected revenue ceiling is estimated at $40 – 50 billion — less than 5% of the combined industry investment over three years.

This points to a structural imbalance: the industry is operating in a mode of perpetual reinvestment, where halting this cycle could mean losing competitive ground. In this high-stakes race for dominance, companies are compelled to continue scaling up, despite a lack of clarity on the long-term trajectory.

The paradox is that these investments are being poured into a technology whose very reliability is already in question — a question that begins, fittingly, on April 1st. If the foundation of AI is data, and that data is increasingly comprised of synthetic, distorted, and nonsensical content, then the entire economic model is being built on a foundation that generates its own fissures. Investing in infrastructure capable of processing trillions of tokens becomes meaningless if those tokens are merely the byproduct of a digital carnival.

Given the systemic limitations of LLMs and the instability of the current economic model, it is evident that the continued advancement of AI necessitates a fundamental shift in architectural approach.

Existing models, based solely on scaling up parameters and data, are hitting a wall of diminishing returns: each new performance increment demands exponentially greater resources, while fundamental problems — hallucinations, a lack of explainability, and catastrophic forgetting — remain unsolved. The recent release of GPT-5 serves as a case in point. After initially touting AGI-level capabilities and postponing the launch multiple times, the result was an underwhelming release, user dissatisfaction, performance degradation on several benchmarks compared to its predecessor, GPT-4o, and even a rollback to previous versions in some cases.

The alternative path, developed by our Mind Simulation Lab for Artificial General Intelligence, proposes a hybrid architecture that combines the strengths of two paradigms:

• The Ascending (Bottom-Up) Approach: This encompasses statistical learning, neural networks, and Natural Language Processing (NLP);
• The Descending (Top-Down) Approach: This involves symbolic systems, logical inference, and structured knowledge.

This symbiotic approach decouples knowledge storage from the reasoning process — a fundamental departure from the LLM architecture, where knowledge is opaquely encoded within the model’s weights and is not amenable to direct modification.

This approach is embodied in Hybrid Knowledge Representation Model technology, implemented as the Embryo of the Digital Brain. This system features a modular architecture with a clear separation of functions:

• Worldview: a multi-layered knowledge repository that stores and organizes information, encompassing abstract categories, facts, events, and data provenance (sources);
• Reflection: an independent reasoning engine that utilizes logical rules and meta-knowledge to perform analysis and inference.

This architectural separation enables the following key capabilities:

• Full Explainability: every step of the inference process can be traced and explained;
• Single-Pass Learning: new data can be ingested and verified in a single pass without full retraining;
• Prevention of Catastrophic Forgetting: knowledge is stored in an isolated repository, protecting it from being overwritten during learning;
• Handling Ambiguity: the system is designed to manage contradictory and uncertain information, which is a hallmark of real-world data.

The crucial advantage is the ability to filter and verify information at the point of ingestion into the knowledge base. Unlike an LLM, which indiscriminately ingests entire texts as given truths, the Embryo can verify a source, assess its credibility, flag information as conditional or satirical, and downgrade the trust level assigned to that source.

This means the system is not "infected" by falsehoods, even when they are pervasive online. It can analyze context, date, source, and other metadata before integrating a fact into its knowledge base.

The history of technology shows that breakthroughs rarely come from simply scaling existing solutions. The steam engine did not become a car by increasing the size of its boiler. The airplane did not evolve from an improved bicycle. Similarly, AGI cannot be achieved by merely increasing the number of parameters in a transformer.

True progress requires rethinking the fundamentals. Instead of just 'pumping' models full of data, we must engineer into them structure, the capacity for reflection, and control over a process of thinking, not just generation.

LLMs, as they exist today, represent a local maximum in the architectural search space — they are effective for specific tasks but incapable of further qualitative growth. Their dependence on synthetic data, lack of explainability, and economic instability all point to a limited lifespan for their role as the most promising path forward. They are destined to become an intermediate stage in the history of AI, and eventually, just another tool in the toolbox.

If an AI cannot distinguish a joke from a fact, if it cannot critically evaluate its sources, if it is trained on data where truth is dissolving into statistical noise — then its lifecycle is capped by the date when falsehood becomes the dominant form of content.

The transition to systems like the Embryo signifies a shift from statistical imitation to cognitive modeling. This is the path where an AI does not merely generate plausible text, but engages in verifiable and transparent reasoning based on a foundation of trustworthy knowledge.

Such systems do not exclude the use of neural networks — for tasks like speech processing, computer vision, or translating natural language into formalized structures. However, the core of intelligence — reasoning, memory, and learning — is built upon an engineered architecture, not on statistical curve-fitting.

Modern LLMs represent a significant stage in the evolution of Artificial Intelligence, but not its culmination. Their successes are accompanied by systemic limitations that cannot be overcome within the current paradigm.

The problems of hallucinations, recursive data contamination, and economic inefficiency all point to the need for an architectural overhaul. AI has taken a wrong turn. The future of AI, especially in the pursuit of AGI, lies not in scaling but in structural integrity, explainability, and controllability.

April Fools' Day is not just a calendar date. It is a symbol of the state AI finds itself in when trained on data devoid of a filter for truth. How many such days it can withstand depends not on GPU power, but on whether the next generation of AI will be capable of understanding context, exercising doubt, verifying information, and learning from its mistakes rather than merely repeating them with escalating amplitude.

Technologies such as the Embryo, developed in our Mind Simulation Lab, represent one possible path toward creating more sustainable, reliable, and effective systems. They do not negate the achievements of LLMs but instead offer an alternative developmental trajectory based on integrating best practices from diverse fields of AI.

The future of AI will be defined not by who trains the largest model, but by who builds the first truly intelligent system — one that does not merely generate text, but understands its meaning.

1. If Even 0.001 Percent of an AI's Training Data Is Misinformation, the Whole Thing Becomes Compromised, Scientists Find — https://futurism.com/training-data-ai-misinformation-compromised
2. OpenAI’s new reasoning AI models hallucinate more — https://techcrunch.com/2025/04/18/openais-new-reasoning-ai-models-hallucinate-more/
3. OpenAI o3 and o4-mini System Card — https://cdn.openai.com/pdf/2221c875-02dc-4789-800b-e7758f3722c1/o3-and-o4-mini-system-card.pdf
4. AI models collapse when trained on recursively generated data — https://www.nature.com/articles/s41586-024-07566-y
5. Long-form factuality in large language models — https://arxiv.org/abs/2403.18802
6. Mind Simulation`s team vision