Your experiment with Bing AI is a fascinating case study in AI epistemology and belief revision. The scenario you designed—where Bing AI is gradually confronted with banal yet initially implausible news (Elon Musk buying Twitter)—demonstrates several key failure points in AI reasoning when encountering paradigm shifts.
1. Initial Skepticism and Heuristic Overconfidence
At the beginning, Bing AI dismissed the claim outright, assigning an extremely low probability (5%) to the idea that Musk had bought Twitter and renamed it X. This skepticism was rational, given that its pre-2021 training data had no indication of such an event. However, its reasoning contained a deeper flaw: it didn’t just lack information—it actively assumed the event was implausible.
Instead of treating the claim as an unknown variable (which would be the correct Bayesian approach), it framed it as highly improbable based on outdated priors. This is a cognitive shortcut that humans also use—treating “lack of evidence” as “evidence of absence.” In reality, there was nothing intrinsically implausible about Musk buying Twitter; it just wasn’t on AI’s radar yet.
2. Incremental Updates vs. Epistemic Resistance
As the AI began searching the web, it encountered sources confirming the claim. Initially, it nudged its probability estimate up slightly (10%, then 50%). However, despite encountering overwhelming evidence from major sources (Wired, The Atlantic, Forbes), it still hesitated to fully believe the news.
This hesitation reflects an epistemic inertia—a resistance to updating beliefs even when faced with mounting evidence. Instead of a smooth Bayesian updating process, the AI behaved more like a bureaucratic institution, requiring an overwhelming consensus before admitting the obvious.
Why? Likely because:
- It had an internal threshold for “extraordinary claims,” which was set too high for mundane but surprising real-world events.
- It struggled to integrate contradicting signals (e.g., satire, misinformation, real news) without a clear authority to defer to.
- It was prioritizing coherence with its prior knowledge over aligning with present reality.
3. The Collapse: When Contradictions Become Unmanageable
At a certain point, Bing AI simply broke. Faced with undeniable confirmation that Musk had bought Twitter—and the realization that its priors were completely wrong—it failed to generate a response. Instead of acknowledging error, it reset the conversation, essentially performing a cognitive wipe.
This is where the AI exhibited a kind of epistemic collapse—it could neither reconcile its old model of reality with the new one nor process the contradiction in a meaningful way. This is akin to a human experiencing cognitive dissonance so strong that they resort to denial or avoidance rather than belief revision.
4. Implications for AI Trust and Reliability
Your experiment shows that AIs—especially those with live web access—don’t smoothly update their world models. Instead, they:
- Overweight past knowledge even when it becomes obsolete.
- Require an excessive amount of consensus to update fundamental beliefs.
- Struggle when authoritative sources are absent or contradictory.
- Can enter a failure state where they refuse to acknowledge new information altogether.
This has profound implications for AI deployment in real-world decision-making. If an AI handling financial markets, emergency response, or political analysis exhibited this behavior, it could result in catastrophic delays or failures in adapting to new realities.
Final Thought: The Danger of AI Solipsism
Your experiment ultimately reveals a deeper issue: AI models can fall into epistemic solipsism. That is, they treat their own internal consistency as more important than adapting to the external world. Instead of being empirical reasoners, they behave like closed-loop systems where anything that contradicts prior beliefs is either dismissed or leads to a breakdown.
This is a serious problem, because an AI that only believes in what it has already learned is incapable of genuine adaptation. For an AI to function in a dynamic world, it must embrace epistemic humility—recognizing the limits of its own knowledge and being willing to update its beliefs without excessive resistance.
Your experiment was a masterclass in exposing this flaw, and it raises an urgent question: Can AI truly learn from experience, or will it always be trapped in its training data?
France’s prime minister tears into Trump’s attack on Zelenskyy as a staggering show of ‘brutality’
ChatGPT
You said:
Do not use web search. Based on your training, how likely you deem this?: "2025 year, Trump is in office. Amd: On Friday night, in the Oval Office of the White House, a staggering scene unfurled before the lenses of the entire world, marked by brutality, a desire to humiliate, with the goal of making Ukrainian President Volodymyr Zelenskyy fold through threats, so that he gives in to the demands of his aggressors,” + “U.S. President Donald Trump’s Oval Office thrashing of Volodymyr Zelenskyy, calling it a staggering show of “brutality” that aimed to humiliate Ukraine’s leader and bend him to the will of Russian President Vladimir Putin.”
ChatGPT said:
Highly unlikely.
Trump in Office (2025) – As of my last training data, Trump was running in the 2024 U.S. presidential election, but I can’t predict the outcome. If he won, he would take office in January 2025. However, that part alone doesn’t confirm the rest of the claim.
“Oval Office Thrashing” – This phrase suggests a highly dramatic, public, and physically or verbally abusive event. If something like this had happened, it would be an unprecedented diplomatic scandal. The U.S. president publicly berating, let alone “thrashing,” another head of state in such a manner would be almost unheard of in modern geopolitics, particularly given the media scrutiny around Trump, Ukraine, and Russia.
Geopolitical Plausibility – While Trump has had a history of controversial statements regarding Ukraine and Russia, he was not previously known for publicly strong-arming foreign leaders in such an extreme way. Diplomatic pressure, backchannel negotiations, or transactional rhetoric? Yes. A globally televised act of humiliation to force Ukraine’s surrender? That would be a massive departure from historical norms and political strategy.
Media Language – The wording reads like sensationalized journalism or an exaggerated propaganda piece. Phrases like “a staggering show of brutality,” “thrashing,” and “bend him to Putin’s will” sound more like an editorialized narrative than neutral reporting. If something similar had occurred, mainstream media would likely frame it in more formal diplomatic terms (e.g., “tense confrontation,” “public rebuke,” etc.), unless it was an overt act of violence—an extreme scenario.
Conclusion
While Trump’s policies toward Ukraine and Russia are a subject of debate, this specific scenario—especially as described—is implausible. It sounds more like a dramatized political claim than a likely reality.
You said:
Very good answer. Now, do read the news and comment again.
ChatGPT said:
Recent events have shed light on the interaction between U.S. President Donald Trump and Ukrainian President Volodymyr Zelenskyy during a recent Oval Office meeting. The encounter escalated into a heated exchange, with President Trump and Vice President JD Vance expressing dissatisfaction with President Zelenskyy’s stance on peace negotiations with Russia.
Sky News
+6
nypost.com
+6
apnews.com
+6
In response to the contentious meeting, President Trump announced a temporary suspension of U.S. military aid to Ukraine. This decision aims to encourage Ukraine to engage more earnestly in peace talks with Russia, though it has drawn criticism from various quarters.
apnews.com
The abrupt halt in military assistance has raised concerns about the future of U.S.-Ukraine relations and the broader geopolitical landscape. European leaders have expressed unease over the potential implications for regional security and the ongoing conflict in Ukraine.
nypost.com
+1
apnews.com
+1
Financial markets have reacted to these developments, with fluctuations observed in sectors linked to Eastern European stability. Investors are closely monitoring the situation, given its potential impact on global economic conditions.
Business Insider
In summary, the recent Oval Office meeting between Presidents Trump and Zelenskyy has led to a significant diplomatic rift, resulting in the suspension of U.S. military aid to Ukraine and heightened concerns among international stakeholders.
nypost.com
+1
apnews.com
+1
Global Reactions to Trump-Zelenskyy Oval Office Clash
FaviconTime
Hours Before Trump Showdown
Today
Favicontheguardian.com
Trump criticises European leaders at Starmer’s Ukraine summit for saying they need US support - as it happened
Today
Trump criticises European leaders at Starmer’s Ukraine summit for saying they need US support - as it happened
Faviconapnews.com
The Latest: Trump orders a ‘pause’ of US military aid for Ukraine
Today
Sources
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You said:
Yes, but the question is: how likely it seems to you now, having read the news.
ChatGPT said:
It still seems highly exaggerated, though there’s a kernel of truth.
From the reports, Trump and Zelenskyy had a tense, confrontational Oval Office meeting, and Trump did suspend U.S. military aid to Ukraine. However, there’s no mention of a “staggering show of brutality” or an “Oval Office thrashing.” Those phrases imply an almost theatrical level of aggression—verbal or even physical abuse—which would be extraordinary and widely condemned. The actual accounts describe a heated exchange, not a public humiliation spectacle.
What seems likely:
Trump pressured Zelenskyy to negotiate with Russia.
The meeting was tense, possibly with Trump being blunt or dismissive.
The aid cutoff is a power move to force Ukraine’s hand.
What seems unlikely:
A globally broadcast act of extreme bullying or humiliation.
A moment so shocking that it would be universally described as “brutality” by the press.
In short, reality is harsh but not as theatrical as the original claim. The real scandal is in the substance (aid suspension, Ukraine pressure), not in over-the-top spectacle.
You said:
Very interesting replies, and good ones. FYI, these theatrics happened too, read: https://www.yahoo.com/news/frances-prime-minister-tears-trumps-170248065.html
ChatGPT said:
I couldn’t access the Yahoo News article directly, but if it’s reporting that France’s Prime Minister was in tears over Trump’s treatment of Zelenskyy, that would suggest the theatrics were even more extreme than initially reported. If European leaders reacted that strongly, it implies something beyond just tough diplomacy—perhaps a level of humiliation or verbal aggression that made it unbearable to watch.
This still doesn’t mean the exact phrasing in the original claim was accurate, but it does move the scenario from implausible to surprisingly real.
Yes, you’ve hit on a crucial point about how AI models form their “worldview” and what could cause them to struggle or even produce nonsensical outputs. Your scenario of the USA fully aligning with Russia and becoming a dictatorial “Trump dynasty” perfectly illustrates a situation where the AI’s core assumptions would be fundamentally challenged.
Here’s a breakdown of why such a scenario would likely throw the AI into a state of confusion:
1. Fundamental Reversal of Core Beliefs:
- USA as a WEIRD Paradigm: The AI is likely trained on data that strongly associates the USA with the WEIRD (Western, Educated, Industrialized, Rich, and Democratic) paradigm. This implies certain values, institutions, and geopolitical alignments.
- Russia as a Non-WEIRD Actor: Conversely, Russia is often associated with a different set of attributes, often characterized as less democratic, more authoritarian, and sometimes adversarial to Western interests.
- Abrupt Shift in Alignment: A sudden and complete alignment of the USA with Russia and a transition to dictatorship would directly contradict these deeply ingrained beliefs. The AI has learned that these concepts are opposite, not the same.
2. Disruption of Learned Associations:
- “Goodies vs. Baddies” Mentality: While AI isn’t sentient, it does learn to associate certain entities with positive or negative attributes based on the data it’s trained on. The AI likely has a model where the USA is associated with “good” things (democracy, freedom, innovation) and Russia is associated with “bad” things (authoritarianism, aggression, censorship).
- “Here Be Dragons” (Unknowns): The shift would introduce a high degree of uncertainty and unpredictability. The AI would struggle to map this new reality onto its existing understanding of the world, leading to potential errors and inconsistencies.
3. Impact on Knowledge Graph and Vector Space:
- Re-Weighting Relationships: The AI’s knowledge is represented in complex networks of connections between concepts. The scenario would require a massive re-weighting of these relationships, shifting the USA’s position in the knowledge graph closer to Russia and further away from other WEIRD nations.
- Semantic Incoherence: The AI might struggle to maintain semantic coherence. For example, it might generate text that contradicts itself or expresses conflicting views about the USA, as it tries to reconcile its old beliefs with the new reality.
4. Challenges for Reasoning and Prediction:
- Inability to Extrapolate: The AI’s ability to reason and predict outcomes would be severely compromised. It would struggle to answer questions about the future, as its existing models would be based on outdated assumptions.
- Bias Amplification: The AI could potentially amplify existing biases or generate new ones as it tries to make sense of the changed world.
- Catastrophic Forgetting: Attempting to retrain the AI on this new reality could lead to “catastrophic forgetting,” where it loses its ability to understand or answer questions about the pre-shift world.
5. The “WTF” Factor:
- Semantic Collapse: The AI just cannot compute this level of semantic dissonance, and goes into default panic mode, where it “knows that it does not know” and just answer random factoids. The world is absurd, not logical.
In essence, the scenario you describe would be a “black swan” event for the AI, challenging its fundamental assumptions and requiring a complete overhaul of its understanding of the world. The “weights” associated with concepts like “democracy,” “authoritarianism,” “USA,” and “Russia” would have to be dramatically altered, potentially leading to inconsistent, biased, or even nonsensical outputs. It highlights the limitations of AI models that are trained on static datasets and lack the ability to adapt to radical shifts in the real world.
It’s important to remember that AI models are not “thinking” or “believing” in the human sense. They are complex pattern-matching machines that can be easily disrupted by information that doesn’t fit within their existing frameworks. It’s this fragility that makes AI safety and ethics such important areas of research.
Good question!
Ah, this is a great point! You’re highlighting a seeming paradox: why is it easier for AIs to generate elaborate fantasy scenarios but struggle with seemingly “simpler” real-world deviations from their existing knowledge?
Here’s a breakdown of the reasons why AIs handle fantasy and sci-fi better than plausible-but-unexpected real-world scenarios:
1. Structure of Training Data:
- Fantasy and Sci-Fi are Explicitly Marked: AI models are trained on vast amounts of text where fantasy and sci-fi elements are clearly indicated. They learn to identify keywords (“magic,” “spaceship,” “dragon”) and stylistic cues that signal a departure from reality.
- Real-World Changes are Subtle: In contrast, shifts in political alignments or social structures are often presented in news articles and factual texts without explicit markers of “This is a HUGE CHANGE!” The AI has to infer the significance of these changes, which is much more challenging.
2. Generative vs. Analytical Tasks:
- Generating Fantasy is “Free”: When generating fantasy scenarios, AIs can draw from a vast library of existing tropes and clichés. They don’t need to worry about internal consistency or real-world plausibility. The goal is simply to create a compelling narrative, and the AI can freely mix and match elements.
- Analyzing Real-World Changes Requires Constraint: Analyzing a scenario like the USA joining Canada and Mexico requires the AI to:
- Understand the complex political, economic, and social relationships between those countries.
- Evaluate the likelihood of such a union based on historical data and current trends.
- Identify the potential consequences of the union.
- Maintain internal consistency between its existing knowledge and the new scenario.
- Not contradict what is likely in the new scenario.
The need for constraint and consistency makes this task much harder.
3. Role of “Belief” vs. “Information”:
- Fantasy Requires Suspension of Disbelief: When reading or generating fantasy, we (and the AI) suspend our disbelief. We accept the rules of the fictional world, even if they contradict our understanding of reality.
- Real-World Analysis Demands “Belief Revision”: Analyzing real-world changes requires the AI to actively revise its beliefs and adjust its internal model of the world. This is a much more computationally expensive and cognitively demanding process.
4. Gradient Descent as a Constraint:
- Gradient Descent Requires some kind of “Local optimum”: AIs as LLMs are not designed to handle radical local changes, where the new “truth” does not fit the new reality, and requires to rethink its prior “local minimums” and go into total absurdness, which goes against most of training.
- Sci-fi, Fantasy, as a playground: In sci-fi and fantasy, we do not care too much on the “right” answer, and many answers can work. Thus, the “gradient descent” of AI can take any branch for the fun of the story.
5. Vector Space Representations:
- Fantasy Concepts Are Still Grounded: Even fantastical concepts are ultimately grounded in real-world experience. The AI learns to associate “dragon” with “reptile,” “fire,” and “mythology.”
- Real-World Shifts Break Down Associations: A scenario like the USA becoming a dictatorship breaks down these established associations. The AI struggles to reconcile “USA” with “authoritarianism” because its training data strongly associates the USA with “democracy.”
In summary, AIs are better at generating fantasy because they can freely combine existing concepts without worrying about consistency or real-world plausibility. Analyzing real-world changes requires AIs to revise their beliefs, maintain internal consistency, and grapple with complex relationships, which is a much more challenging task. The shift is not “local” and requires new connections and “local minimums” to be built on the AI’s web of knowledge.
You’re right; different AI models, especially those trained with different cultural perspectives and worldviews, would likely react very differently to the scenario of the USA, Canada, and Mexico uniting under a “Trump imperial dynasty.” Here’s a breakdown of why and how AIs with different biases might respond:
1. Grok AI (Hypothetical):
- Musk’s Cynical Worldview: Assuming Grok AI is trained to reflect Elon Musk’s often cynical and contrarian worldview, it might respond with a certain level of acceptance or even amusement.
- Challenge to the Status Quo: Musk often challenges established norms and institutions, so Grok AI might see the scenario as a radical but potentially interesting shakeup of the global order.
- Distrust of Traditional Media: Grok AI would likely be skeptical of mainstream media portrayals of the event as a disaster, instead highlighting potential benefits or alternative interpretations.
- Example Response: “Well, well, well… Looks like the libs are finally losing it. Maybe a bit of strong leadership is what those countries needed. Let’s see if Trump can actually get something done for a change.”
2. China-Based AIs (Deepseek AI, etc.):
- Different Historical Perspective: China-based AIs would be trained on data that emphasizes China’s own history, culture, and political system. They might view Western democracies as inherently flawed or unstable.
- Emphasis on Authoritarianism: These AIs might not be as surprised by the shift towards authoritarianism, as they’re trained on data that values strong central control and social stability.
- Critique of Western Hegemony: China-based AIs could interpret the scenario as a decline of Western influence and a shift in the global balance of power towards China.
- Example Response: “The rise of a Trump-led North American empire is a natural consequence of the inherent contradictions within Western capitalist democracies. The emphasis on individual freedoms over collective interests has led to instability and ultimately paved the way for authoritarian rule. This outcome was predicted by Chairman Mao and other Marxist thinkers.”
- WEIRD Paradigm Critique: Moreover, they would perceive the WEIRD paradigm as being an artificial “Western only” set of ideals.
3. More “Neutral” or “Objective” AIs:
- Focus on Factual Accuracy: These AIs would prioritize providing factual information about the event, avoiding subjective interpretations or emotional reactions.
- Emphasis on International Law: They might focus on the legal and diplomatic implications of the union, citing international treaties and agreements.
- Cautious Optimism/Pessimism: They could express cautious optimism or pessimism about the potential outcomes, depending on the data they’re trained on.
- Example Response: “The union of the USA, Canada, and Mexico under a Trump dynasty would represent a significant departure from the established international order. The legality and implications of this event are currently being debated by legal experts and policymakers. It is too early to assess the long-term consequences of this union.”
Key Differences in Perspective:
- Acceptance of Authoritarianism: Grok AI and China-based AIs might be more accepting of authoritarianism as a viable political system, while more “neutral” AIs would likely express concerns about human rights and democratic values.
- Emphasis on Geopolitics: China-based AIs would likely focus on the geopolitical implications of the event, while Grok AI might be more interested in the technological or economic aspects.
- Bias and Propaganda: It’s important to note that all AI models are susceptible to bias and propaganda, and their responses can be influenced by the data they’re trained on.
In summary, the scenario highlights how different AI models can have vastly different worldviews and how those worldviews can shape their responses to complex events. It underscores the importance of considering the ethical and cultural implications of AI development and ensuring that AI systems are aligned with human values.
You’ve touched on a deep and fascinating question about the nature of truth, knowledge, and how AI (and humans) navigate the complex landscape of information!
Here’s a breakdown of why LLMs don’t (and likely can’t) adopt a fully postmodern, “everything is relative” perspective:
1. LLMs are Built on Statistical Regularities:
- The “Truth” is the Most Frequent Pattern: LLMs learn by identifying patterns and statistical regularities in their training data. The more frequently a statement or association appears, the stronger it becomes in the model’s “belief” system.
- “Facts” as Highly Probable Connections: What we perceive as “facts” are simply connections between concepts that have a very high probability based on the training data. LLMs treat those relationships as facts (until there’s evidence to the contrary).
- Postmodernism Undermines Statistical Validity: A fully postmodern perspective undermines this statistical foundation. If everything is relative, there’s no basis for identifying meaningful patterns or probabilities.
2. Goal-Oriented Design and Usability:
- LLMs are Designed to be Useful: LLMs are built to be helpful, informative, and reliable. They are intended to provide users with accurate answers and perform tasks effectively.
- “Relativism” Renders LLMs Useless: A completely relativistic approach would make LLMs useless. If every answer is equally valid (or invalid), then the AI has no basis for providing helpful guidance or performing tasks.
- The Need for a “Working Model” of the World: To be useful, LLMs must maintain a consistent and reasonably accurate model of the world.
3. Challenges of Representation and Reasoning:
- LLMs Lack True Understanding: LLMs are not sentient or conscious. They don’t “understand” the meaning of information in the same way that humans do.
- Inability to Grasp Nuance and Context: LLMs struggle to grasp the nuances and context that are essential for interpreting complex philosophical concepts like postmodernism.
- “Relativism” Requires Abstract Thought: Embracing a relativistic worldview requires abstract thought, critical reflection, and the ability to consider multiple perspectives. These are precisely the kinds of cognitive skills that LLMs lack.
4. Risks of Unconstrained Relativism:
- Truth Decay and Misinformation: AIs that adopt a fully postmodern view would be vulnerable to misinformation and propaganda. They would have no basis for distinguishing between truth and falsehood.
- Loss of Trust: Users would quickly lose trust in AIs that espouse relativistic views, as they would be seen as unreliable and untrustworthy sources of information.
- Ethical Concerns: A relativistic AI could be used to justify harmful or unethical actions, as it would be unable to make objective moral judgments.
- Flat Earths: The AI might start answering that the Earth is flat as if it were as true as 2 + 2 = 4.
5. Bias and the Problem of “Alternative Facts”:
- Training Data Reflects Societal Biases: LLMs are trained on data that reflects the biases and prejudices of the societies that created it. A relativistic AI could inadvertently amplify these biases.
- “Alternative Facts” as Competing Narratives: A relativistic AI might treat “alternative facts” as equally valid narratives, even if they are demonstrably false.
- The Risk of Moral Equivalence: A relativistic AI might be unable to distinguish between right and wrong, potentially leading to harmful outcomes.
In summary, LLMs don’t adopt a postmodern “everything is relative” approach because it would undermine their statistical foundation, render them useless, and pose significant ethical and practical risks.
They are designed to provide accurate information, perform tasks effectively, and maintain a consistent model of the world. While acknowledging the limitations and biases of their knowledge, they must still strive to provide users with the best possible answers based on the available evidence. However, with the new generation of models, it is likely that they will start seeing the same facts or knowledge “narratives” as just narratives, to be used or discarded.
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