@Cavalot read this and get back to me. I don't hate AI, I hate the proliferation of use for which it still isn't up to par. Thanks.
(Disclaimer, I didn't read all that shit. I assume it's good)
AI Models Lack Real-Time Awareness and Contextual Sensitivity
At the most fundamental level, AI language models—regardless of how advanced they appear—are not aware of the present moment. They do not know what is happening right now. Even when connected to real-time tools or browsing capabilities, their understanding is limited to what they can parse from available digital text, which may be outdated, biased, incomplete, or outright false. Unlike a trained journalist or subject matter expert who can evaluate a developing situation in context, AI models merely interpret fragments of text based on statistical associations. They do not comprehend the event, its implications, or the broader context in which it’s unfolding. This limitation makes them inherently unreliable as arbiters of truth in fast-moving or controversial news cycles.
Furthermore, current events often involve conflicting narratives, incomplete data, and rapidly shifting facts. What is “true” one hour may become “false” the next. AI models are not built to handle this kind of temporal instability. Their training data is always stale by definition, with a knowledge cutoff that renders them oblivious to the very nature of live events. Even when tools are layered on top to allow browsing or limited search, the AI cannot distinguish between high-quality, fact-checked reporting and manipulative or misleading content. It simply mimics language patterns that appear plausible. As a result, even when AI gives answers that seem logical or reasonable, those answers are often shallow approximations, stripped of the analytical depth and cross-referenced verification that real-world truth-seeking demands.
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AI is Prone to Hallucination and Fabrication
Another major reason to distrust AI in this space is the well-documented phenomenon known as "hallucination" — where models generate entirely false information that appears factually correct on the surface. These fabrications can include fake statistics, invented quotes, nonexistent sources, or even events that never happened. Crucially, the model doesn't "lie" in the human sense; rather, it generates output that fits linguistic patterns, regardless of whether the facts are grounded in reality. This makes hallucinations particularly insidious because they are delivered with the same confident tone and fluent style as accurate information. For users unfamiliar with a topic, there’s no obvious signal that the model is making things up.
In the domain of current events, where factual precision is essential, hallucinations can cause significant harm. Imagine an AI confidently claiming that a particular country has launched a military strike, or that a public health agency has declared an outbreak, when in fact no such thing has occurred. In an era already plagued by disinformation and distrust in media, the injection of artificially-generated falsehoods—even unintentional ones—only further erodes our collective ability to agree on basic facts. Unlike traditional media, where sources can be traced and journalists held accountable, AI outputs offer no such transparency or responsibility. The result is a “black box” of answers that cannot be independently verified by the user, even when those answers carry significant real-world consequences.
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Logical Consistency is Superficial and Easily Broken
While AI may appear logically coherent, its logic is often superficial and prone to collapse under scrutiny. Language models do not reason in the traditional human sense. They do not use deductive or inductive reasoning based on underlying principles or facts; rather, they reproduce patterns of language that resemble logic. This means that AI can produce contradictory statements depending on how a question is phrased, who it imagines the "audience" to be, or even the random sampling behavior of the model at that moment. The illusion of consistency is strong—particularly when responses are long, well-structured, and grammatically correct—but the underlying mechanism is not reasoning; it is linguistic mimicry.
This becomes especially dangerous when dealing with politically charged or ethically complex current events. On one hand, the model may try to appear neutral, giving balanced viewpoints; on the other, it may subtly inject false equivalence, logical fallacies, or misleading framings that distort understanding. Because the AI lacks a genuine grasp of what it is saying, it cannot detect its own logical flaws, nor can it defend its reasoning if challenged. A user who lacks the time or expertise to critically interrogate AI output might take these responses at face value, unaware that the structure of the argument may be built on misrepresented premises or outright fabrications. In this way, the model’s "logic" is a performance—persuasive in tone, but unreliable in substance.
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No Accountability Means No Consequence for Being Wrong
In the human world, authors, reporters, experts, and analysts can be held accountable for their errors, biases, and failures. They can be corrected, discredited, or even sued. AI, however, exists outside this framework. When an AI gives you a wrong or misleading answer about a current event, there is no accountability—no byline, no editorial process, no institutional oversight. Even if the output causes harm or spreads falsehoods, the responsibility is ambiguous at best: Is the blame on the user who asked the question? The developer who trained the model? The company that deployed it? This lack of clarity leads to a dangerous situation in which AI can make authoritative-sounding claims without bearing any consequence for their veracity.
In practice, this means that AI can produce statements that sway opinions, reinforce biases, or even provoke conflict—without ever being subject to correction or retraction. While developers may try to patch particularly egregious issues through updates or content filters, the core model remains vulnerable to subtle and systemic errors. In an information ecosystem already burdened by clickbait, deepfakes, and echo chambers, adding a tool that mimics authority without being accountable to truth only worsens the crisis of trust. AI may be useful for brainstorming, summarizing, or exploring ideas, but when it comes to hard facts about unfolding events, relying on an unaccountable system is a dangerous gamble.
