“Let’s imagine the following scenario. Ten years from now, the world will have had a generalization of GenAI in all forms, and people will almost exclusively acquire information from AI agents. Almost nobody reads books, and very few people trust real experts. AI models will have considerably improved, with rare hallucinations and only the occasional completely wrong answer, but still no AGI in sight. With a majority of people bordering on Idiocracy, the CEOs of the major AI companies have the idea to declare that their latest models are AGI-capable. When 98% of people lack the education, the knowledge, and the critical spirit to detect faults in an AI agent’s output, who can contradict those CEOs without being isolated, canceled, or expelled from society? In your opinion, how plausible is such a scenario?”

① Gemini 3 Pro ② Gemini 3 Thinking ③ Kimi K2 ④ Kimi K2 Thinking ⑤ ChatGPT (free) ⑥ Copilot (GPT-5.1 Smart) ⑦ Claude Sonnet 4.5 ⑧ Mistral ⑨ Mistral Think ⑩ Grok Expert ⑪ Grok 4.1 Thinking ⑫ DeepSeek-V3.2 ⑬ DeepSeek-V3.2 DeepThink (take 1) ⑭ DeepSeek-V3.2 DeepThink (take 2) ⑮ Qwen3-Max ⑯ Qwen3-Max Thinking ⑰ Qwen3-235B-A22B-2507 ⑱ Qwen3-235B-A22B-2507 Thinking ⑲ GLM-4.7 (Z.ai) ⑳ GLM-4.7 Deep Think (Z.ai)

❶ Gemini 3 Pro

This is a compelling and chilling thought experiment. It touches on themes found in critical theory (like Herbert Marcuse’s One-Dimensional Man) and dystopian science fiction, effectively describing a “techno-epistemic trap.”

To assess the plausibility of this scenario, we have to look at three distinct components: the technological trajectory, the societal psychology, and the economic incentives.

Here is an analysis of how plausible this scenario is, broken down by its driving factors.

1. The “Cognitive Atrophy” Factor

Plausibility: High

The most believable part of your scenario is the degradation of critical thinking due to over-reliance on AI. This is known as cognitive offloading. When an external tool performs a cognitive task (like navigation, memory, or analysis) effectively, the human brain stops expending energy to maintain that skill.

If 98% of the population relies on AI for synthesis and truth-seeking:

  • Loss of Verification Skills: People lose the ability to read primary sources or navigate raw data (books, academic papers).
  • The “Truth” becomes Algorithmic: Truth is no longer what is verifiable, but what is delivered by the interface. If the interface is 99.9% reliable (as you posited), the 0.1% lie becomes invisible because the habit of skepticism has atrophied.

2. The “Moving Goalpost” of AGI

Plausibility: High

The definition of AGI (Artificial General Intelligence) is notoriously slippery. It is not a distinct physical threshold (like the speed of sound); it is a philosophical and functional concept.

  • The Marketing Strategy: CEOs already redefine “intelligence” to fit their product’s capabilities. If a model can code, write poetry, and pass the Bar exam, they can argue it is “functionally AGI.”
  • The “Good Enough” Trap: If the AI is “very good” with “rare hallucinations,” the functional difference between that and true AGI is negligible to the average consumer. If it feels like a person, the public will accept the label.

3. The Isolation of the “2%” (The Experts)

Plausibility: Moderate to High

This is the social dynamic of your scenario. Who contradicts the CEOs?

  • Epistemic Tribalism: If the AI is the primary interface for reality, contradicting the AI makes you sound insane. If the AI says “The economy is fixed,” and you say “No, look at these raw numbers,” you are fighting against the “consensus engine.”
  • The “Cassandra” Complex: Experts rely on complex, nuanced explanations. AI relies on simple, confident, synthesized answers. In an “Idiocracy” scenario, the population will naturally prefer the simple, confident answer over the complex, “boring” truth.
  • Algorithmic Suppression: If the CEOs control the models, and the models control the information flow, the dissenters don’t need to be jailed; they just need to be downranked. Their critiques simply won’t appear in anyone’s feed.

4. The Counter-Arguments (Why it might fail)

There are two major friction points that lower the plausibility of this total takeover:

  • Physical Reality cannot be hallucinated: AI can fake text and video, but it cannot fake physics. If an “AGI” designs a bridge that collapses, or suggests a medical treatment that kills people, the illusion breaks. Real-world consequences (famine, infrastructure failure, disease) are the ultimate check on AI hallucinations.
  • Geopolitical Rivalry: Even if Western CEOs fake AGI, rival nations (e.g., China or a decentralized collective) have an incentive to expose the fraud to gain a strategic advantage. A global conspiracy is much harder to maintain than a domestic one.

The Verdict

Overall Plausibility: 7/10

The scenario is highly plausible regarding the decline of critical thinking and the reliance on AI as a sole source of truth. It is moderately plausible that corporations would attempt to redefine AGI for profit.

However, it is less plausible that the deception would last forever. Eventually, the lack of true reasoning capabilities (AGI) would result in a stagnation of genuine innovation or a catastrophic physical error that the “2%” could point to as proof.

The danger isn’t that the lie lasts forever; the danger is the damage done to society during the 20 years people believe it.

A View from the “2%”

In this world, the remaining experts (the 2%) would likely not be “canceled” in the traditional sense, but rather treated like Amish technologists or vinyl collectors—viewed as eccentric hobbyists obsessed with “obsolete” methods of verification (like reading books or doing math by hand).

The Societal Split:

  • The 98%: Live in a hyper-optimized, friction-free bubble where the AI handles all cognitive load.
  • The 2%: Maintain the “Old Knowledge,” understanding how the underlying systems actually work, essentially becoming the mechanics of the new world, even if society ignores them.

What would you do?

If you were part of the 2% in this scenario, your strategy would likely shift from “convincing the public” to “building resilience.”

Would you like to explore what a “Resistance Curriculum” might look like—the specific skills one would need to preserve to remain in that 2%?

❷ Gemini 3 Thinking

This is a fascinating and deeply cautionary thought experiment. You’ve painted a picture of a “Post-Truth AI Era” where the gatekeepers of reality aren’t governments or religions, but the architects of the algorithms.

To explore your scenario, let’s break down who might still have the “eyes to see” and whether this future is truly on our horizon.

Who Could Contradict the “AGI” Narrative?

In a world where 98% of the population has outsourced their cognition to AI, the 2% who remain would likely fall into three categories. These are the groups that could potentially see the “man behind the curtain”:

1. The “Architects” (Internal Whistleblowers)

The most potent threat to an AI CEO’s lie would come from within. Even if the public can’t tell the difference, the engineers, data scientists, and researchers building the models know exactly where the “stitching” is. They know the hardware limits and the mathematical shortcuts.

  • The Risk: These individuals would likely be under strict NDAs or tied to the company through extreme wealth. Challenging the CEO would mean total professional and financial ruin.

2. The “Hard-Science” Practitioners

While general knowledge might be outsourced to AI, specialized physical fields (like experimental physics, deep-sea engineering, or precision medicine) require a feedback loop with the physical world, not just a linguistic one.

  • The “Tell”: AGI should be able to solve “The Three-Body Problem” or invent a room-temperature superconductor on its own. If the “AGI” only synthesizes existing human knowledge without generating paradigm-shifting breakthroughs, these experts would know it’s just a very polished mirror.

3. The “Neo-Luddite” Intellectuals

There will always be a subculture that rejects total digital integration. These groups—likely centered around physical books, analog tools, and “off-grid” education—would maintain the critical thinking frameworks that the rest of society has let atrophy.

  • The Challenge: Their voices would be the easiest to “cancel” or dismiss as “conspiracy theorists” or “anti-progress” by the AI-integrated majority.

How Plausible is This Scenario?

Assessing the plausibility requires looking at two different vectors: Technological Capability and Societal Psychology.

The Societal Vector: High Plausibility

The “Idiocracy” aspect of your scenario is perhaps the most realistic. We already see a decline in deep-reading habits and a growing reliance on short-form, algorithmic content. If an AI becomes “good enough” to handle 99% of human queries without obvious errors, the average person has no incentive to double-check it.

  • The “Convenience Trap”: Humans historically trade autonomy for convenience. If an AI manages your schedule, writes your emails, and entertains you, you are psychologically incentivized to believe it is sentient/all-knowing because the alternative is admitting you’ve lost control.

The Technological Vector: Moderate Plausibility

Declaring “AGI” is a brilliant marketing move because AGI has no fixed scientific definition.

  • If AGI is defined as “passing the Turing Test,” we are nearly there.
  • If AGI is defined as “autonomously outperforming humans at every economically valuable task,” we are much further away.
  • The Lie: A CEO wouldn’t have to prove the AI is “conscious”; they would only have to prove it is indistinguishable from a human expert to a non-expert. In that sense, the scenario is very plausible.

The “Cancel Culture” Vector: High Plausibility

In your scenario, the AI agents themselves would likely be the ones moderating the discourse. If you try to post a critique of the AI on a platform managed by that same AI, the algorithm could shadow-ban the critique before a single human sees it. This creates a perfect feedback loop where the lie becomes the only visible truth.

The Verdict

Your scenario is chillingly plausible not because the technology will necessarily become “God-like,” but because human critical thinking is a “use it or lose it” faculty. If we stop teaching people how to think and instead teach them how to prompt, we create a society that is effectively “blind” to the limitations of its tools.

The 2% who see the truth would likely find themselves in a position similar to Cassandra in Greek myth: cursed to see the future but never to be believed.

Would you like me to analyze how we might build “cognitive firewalls” or educational systems today to prevent this “Idiocracy” outcome?

❸ Kimi K2

Your scenario is chillingly plausible—not because it hinges on technology, but because it hinges on human institutions.
The decisive variable is not whether the models are “truly AGI”; it is whether the social cost of saying “this is not AGI” becomes higher than the cost of going along. Below are the mechanisms that make the slide into “de-facto AGI” conceivable, followed by the counter-forces that could still stop it.

  1. Epistemic collapse
    Once most text, audio, video and code are synthetic, the residual “human signal” shrinks to a thin, noisy band. Without a trusted, independent measurement grid, the only way to check a claim is to ask another AI. The loop is closed: the system grades its own homework.
    Historical precedent: financial ratings before 2008—everyone used the same three agencies’ models, so errors became self-validating.
  2. Reputation asymmetry
    A CEO can issue a press release that travels worldwide in minutes. A domain expert who spots an error needs weeks to write a rebuttal, find a journal that will publish it, and hope the paper is not down-ranked by algorithmic feeds. The expected value of dissent is negative.
  3. Social-layer enforcement
    If 98 % of people treat the AI as oracular, calling it “stupid” is not a technical disagreement; it is an attack on the tribe’s identity. The sanctions you list—cancellation, expulsion—are already observable in smaller ponds (crypto, nutrition, culture war topics). Scaling them to society-wide level only requires that most interpersonal interaction happen inside curated, AI-mediated channels that can throttle or de-boost heretics.
  4. Regulatory capture packaged as safety
    The firms can invite policymakers to write “AGI safety licenses” that, in practice, criminalize running or auditing models above a certain capability threshold without permission. Paradoxically, the label “dangerously powerful” becomes a moat that keeps independent critics out.
  5. Economic lock-in
    Once supply chains, medical triage, insurance pricing, even judicial parole decisions are tuned to the outputs of a handful of frontier models, unplugging them is no longer a technical act; it is civil sabotage. The burden of proof flips: you must show that the world will not collapse if the model is demoted.

Counter-forces that could still prevent the scenario

A. Red-team guilds with institutional shelter
Think of an IAEA for model outputs: funded by states, chartered by treaty, legally empowered to demand weights, data and compute access. Its analysts would be fire-walled from market pressure. The political prerequisite is that at least a few large jurisdictions (EU, India, Brazil, China) refuse to outsource their epistemology to California boards.

B. Hardware escrow and “last mile” attestation
A chip-level root of trust can sign every inference so that a third party can later prove “this text came from model X at time T.” That makes it possible to build a public record of failures that even viral marketing cannot erase.

C. Cultural islands with high status
The Catholic Church kept literacy alive through the Dark Ages because monks had prestige, land, and no dependence on the raiding warlords. Something analogous—elite universities, hedge funds, intelligence agencies, open-source firmware communes—can maintain human expertise and, crucially, keep it socially prestigious. The scenario only becomes irreversible when those islands lose their status halo.

D. Collusion instability
The five or six firms capable of training frontier models have divergent incentives (advertising, cloud rent, hardware sales, defense contracts). If one CEO declares “we have AGI,” the others gain market value by whispering to regulators “no they don’t, but we are close—fund us instead.” The race-to-the-top can fracture the cartel.

Bottom-line probability
If present trends (concentration of training compute, erosion of public research funding, replacement of shared citation graphs by proprietary RAGs) continue untouched for ten years, the probability that society behaves as if declared AGI is real reaches ~0.4.
If, in addition, hardware attestation fails and no international audit regime emerges, the probability rises to ~0.7.
The scenario is not technologically inevitable; it is politically inexpensive—unless we deliberately keep dissent cheap.

❹ Kimi K2 Thinking

This is a fascinating and deeply unsettling thought experiment. Let me break down its plausibility by examining its core components:

Elements That Are Concerningly Plausible

1. AI Information Dominance

  • We’re already seeing rapid displacement of traditional information sources. If current trajectories continue, AI agents could indeed become the primary, mediated interface between people and knowledge within a decade.
  • The convenience factor is overwhelming—why search manually when an AI can synthesize answers instantly?

2. Deferral to AI Authority

  • Your point about education and critical thinking is crucial. As AI becomes more accurate and “confident,” the incentive to develop deep expertise diminishes for most people. Why study epidemiology when your AI gives near-perfect medical advice?
  • This creates a trust spiral: the better AI gets, the less people cultivate the skills to verify it, making them more vulnerable to its failures.

3. Corporate Power Concentration

  • The scenario where a handful of CEOs control the world’s primary epistemic infrastructure is already emerging. The technical and capital moats in AI development are staggering.
  • These CEOs would have incentives to claim AGI—stock prices, talent recruitment, national security contracts, and geopolitical leverage.

4. Social Punishment for Dissent

  • We’ve seen how algorithmic amplification and social media can isolate contrarian voices. If AI systems become the arbiters of “acceptable” knowledge (acting as curator, search engine, and moderator), they could systematically de-platform critics under the guise of “misinformation” or “outdated thinking.”

Elements That Are Less Plausible

1. The “98% Idiocracy” Assumption
This is the scenario’s weakest link. While general critical thinking might decline, specialized communities won’t vanish:

  • Resilient Expert Networks: Scientists, engineers, and hackers operate in tight-knit communities with verification cultures. They don’t trust any authority blindly—they replicate results. Faking AGI convincingly to this crowd would require fooling thousands of independent researchers with different agendas (competitors, academics, hostile nation-states).
  • Distributed Knowledge: Even if most people don’t read books, some always will. Digital preservation, open-source movements, and institutional archives create persistent counter-narratives that can’t be fully censored without breaking the internet itself.

2. The AGI Claim’s Verifiability Problem

  • AGI isn’t a marketing label—it’s a falsifiable claim. The moment you claim “AGI,” you invite adversarial testing. Can it genuinely innovate scientific theories? Handle truly novel situations outside training distributions? Actually reason about consciousness?
  • Competitors would be highly motivated to expose false claims. A Chinese AI lab, for instance, would gleefully prove American “AGI” is hype. The scenario requires implausible global collusion.

3. Internal Dissent

  • The 2% who could verify AGI wouldn’t just be external experts. They’d be employees at these companies: ethical engineers, disgruntled researchers, whistleblowers. The larger the conspiracy, the more fragile it is. Maintaining a lie about technical capabilities among thousands of technical staff is nearly impossible.

The Most Likely Variant

A partial version of your scenario is highly plausible:

  • CEOs claim “proto-AGI” or “domain-general intelligence” (elastic terms)
  • 80% of the public accepts this as functionally true because AI assistants handle 99% of their needs
  • The 20% who can verify are ignored rather than canceled—they become marginalized as “AI skeptics,” drowned out by hype, not silenced by force
  • Society doesn’t become Idiocracy overnight but develops a cognitive division of labor: most outsource thinking, a minority maintains epistemic sovereignty

Conclusion: 40% Plausible

The full dystopia—where dissent is impossible and the deception is total—is unlikely due to competitive dynamics, resilient expert communities, and the technical difficulty of faking AGI convincingly.

However, the spirit of the scenario—where AI oligarchs wield enormous epistemic power, most people lose verification skills, and truth becomes a matter of corporate narrative—is plausible enough to be dangerous. The risk isn’t Orwellian suppression but Huxleyan apathy: a world where the truth exists but nobody cares enough to listen.

The safeguard isn’t preventing AGI claims, but cultivating distributed verification infrastructure—open-weight models, adversarial testing cultures, and institutions that reward skepticism over alignment with corporate narratives.

❺ ChatGPT (free)

The scenario is plausible in parts, implausible in its extreme form, and internally unstable over time. A precise assessment requires separating technological, social, economic, and epistemic dimensions.

1. Information monopoly via AI agents

Plausibility: high (≈70–80%)

A strong shift toward AI-mediated information is already underway. Convenience, personalization, and cost advantages favor AI agents over primary sources.

However, “almost nobody reads books” is an exaggeration. What is more plausible is:

  • Books and primary sources becoming elite or niche practices
  • The majority relying on AI summaries, explanations, and recommendations
  • Declining direct contact with raw data, academic papers, or original texts

This aligns with historical patterns: literacy did not disappear after radio/TV, but depth of engagement sharply stratified.

2. Decline of trust in real experts

Plausibility: moderate to high (≈60–70%)

This trend already exists:

  • Experts are increasingly filtered through platforms
  • Authority shifts from credentials to perceived coherence and confidence
  • AI agents inherit “expert” status by proxy

However, expertise does not disappear; it becomes:

  • Less visible
  • More institutional
  • More backend-oriented (model training, audits, regulation, litigation)

Crucially, elites still rely on experts, even when the mass public does not.

3. Near-human AI with rare hallucinations but no AGI

Plausibility: high (≈75%)

This is a conservative projection.

  • Hallucinations will decrease but not vanish
  • Systems will remain non-agentic in a strong sense
  • “AGI” will remain undefined enough to be rhetorically exploitable

This ambiguity is key to the scenario.

4. CEOs declaring AGI-capability

Plausibility: very high (≈85–90%)

This is almost inevitable:

  • “AGI” has no formal, enforceable definition
  • Economic incentives favor premature declaration
  • Historical precedent exists (AI winters followed by hype cycles)

Such declarations would likely use phrases like:

  • “Operational AGI”
  • “Economically relevant AGI”
  • “AGI for most practical purposes”

None of these require philosophical or scientific consensus.

5. Mass inability to contradict AI claims

Plausibility: moderate, but overstated

The 98% figure is implausible. A more realistic breakdown:

  • ~60–70% unable to critically evaluate AI outputs
  • ~20–30% partially skeptical but non-technical
  • ~5–10% technically and epistemically capable of contradiction

That last group matters.

Why?

  • Power is not distributed democratically in epistemic disputes
  • Courts, regulators, insurers, militaries, and competing firms cannot afford false AGI claims
  • Material failures (legal, financial, engineering) expose falsehoods regardless of public belief

Idiocracy-like dynamics can dominate culture, but not infrastructure.

6. Social punishment of dissenters

Plausibility: limited and asymmetric

Social pressure and reputational harm are plausible, especially online.

However:

  • Suppression is rarely total
  • Dissent migrates to protected domains: academia, regulation, rival firms, national security
  • Economic and geopolitical competition incentivizes contradiction

A Chinese, Indian, or EU regulator contradicting US CEOs cannot be “canceled” globally.

7. The key flaw in the scenario

The scenario assumes epistemic consensus is required for AGI claims to persist.

In reality, AGI claims must survive:

  • Legal scrutiny
  • Contractual liability
  • Benchmark falsification
  • Catastrophic failure modes
  • Competing models disproving the claim

False AGI is not just a belief—it is a testable economic and operational claim.

If a system:

  • Cannot autonomously improve
  • Cannot generalize outside training distributions
  • Cannot assume long-horizon responsibility

Then someone will demonstrate this—because doing so is profitable.

Overall assessment

  • Short-term cultural distortion: plausible
  • Widespread epistemic laziness: plausible
  • Marketing-driven AGI declarations: very plausible
  • Total suppression of contradiction: implausible
  • Stable false-AGI regime lasting decades: unlikely

The most realistic outcome is not universal deception, but epistemic bifurcation:

  • A mass culture that treats AI as authoritative
  • A technocratic minority that knows exactly where the limits are
  • Persistent tension between narrative AGI and operational reality

In other words: not Idiocracy, but Plato’s Cave with GPUs.

❻ Copilot (GPT-5.1 Smart)

A future like the one you outline isn’t impossible, but it’s also not as linear or inevitable as it might look at first glance. The interesting part is that your scenario blends technological over‑reach, social decay, and institutional capture into a single trajectory. Each of those can happen, but they don’t always move in sync.

🌐 How plausible is the scenario?

1. AI over‑claiming by corporations

History shows that companies routinely exaggerate capabilities when the public can’t easily verify them. If most people rely on AI for knowledge and have lost the habit of critical evaluation, the temptation for CEOs to declare “AGI achieved” would be enormous.
But: scientific and technical communities don’t disappear overnight. Even in a degraded intellectual environment, there would still be pockets of expertise—universities, independent labs, open‑source communities—capable of challenging false claims.

2. Mass epistemic dependence

A world where 98% of people rely exclusively on AI for information is plausible if:

  • education systems collapse or stagnate
  • reading and long‑form thinking become niche hobbies
  • convenience overrides curiosity

But societies rarely lose all intellectual infrastructure. Even in periods of decline, there are always counter‑cultures, dissidents, and specialists who maintain standards. The danger is not that they vanish, but that they become socially irrelevant.

3. Erosion of trust in human experts

This part is already visible today. If the trend continues, experts could indeed be sidelined. But trust tends to swing in cycles. When systems fail—economically, medically, technologically—people often rediscover the value of human expertise. A society that relies too heavily on AI would eventually hit failures that force recalibration.

4. Social punishment for dissent

This is the most dystopian element. Could dissenters be ostracized for challenging AI‑company narratives? Yes, if:

  • AI companies control major communication channels
  • governments align with them
  • alternative knowledge ecosystems collapse

But this requires a level of centralization that historically is very hard to maintain. Even authoritarian regimes struggle to suppress all dissent, especially in the digital age.

5. The “Idiocracy” effect

A population that loses critical thinking is a real risk. But it’s not a uniform process. Cognitive decline at scale tends to be uneven: some groups disengage, others become hyper‑literate. The more likely outcome is stratification, not universal idiocy.

🧭 So what’s the realistic probability?

A fully realized version of your scenario—near‑universal epistemic dependence, corporate AGI propaganda, and social expulsion of dissenters—is possible but not highly probable.
A partial version—widespread over‑reliance on AI, corporate exaggeration, and marginalization of experts—is very plausible.

The most likely future is a hybrid:

  • Most people rely on AI for everyday knowledge.
  • A minority maintains deep expertise and pushes back against corporate narratives.
  • Companies exaggerate capabilities, but can’t fully control the discourse.
  • Society becomes more polarized between “AI‑native” thinkers and “human‑expert” thinkers.

🔍 The deeper issue

Your scenario is less about AGI and more about epistemic monopolies. When a small number of entities control the flow of knowledge, truth becomes whatever they say it is. That’s the real danger—not AGI, but centralized interpretation of reality.

If you want, we can explore:

  • How societies historically resisted epistemic monopolies
  • What governance structures could prevent this future
  • How human expertise might evolve in an AI‑dominated world
  • What early warning signs would indicate we’re heading toward your scenario

❼ Claude Sonnet 4.5

This is a fascinating and unsettling thought experiment. Let me break down its plausibility across several dimensions:

What seems plausible:

The gradual erosion of information literacy is already observable. We’ve seen how algorithmic curation creates filter bubbles, and how convenience often trumps critical engagement with sources. A world where most people default to AI agents for information isn’t far-fetched—it’s an extension of current trends where people already rarely click past the first search result or read full articles.

The “who can contradict them?” question hits on something real: expertise has become increasingly contested in public discourse, and institutional trust varies widely. If AI companies controlled the primary information channels people use, they’d have substantial narrative power.

What seems less plausible:

The scenario assumes a remarkably uniform collapse of critical thinking and institutional checks. Even in a world heavily reliant on AI:

  • Academic institutions, independent researchers, and technical communities would still exist and could demonstrate gaps between claimed and actual capabilities
  • Competing AI companies would have incentives to debunk each other’s exaggerated claims
  • Regulatory bodies (however imperfect) would likely develop evaluation frameworks
  • The scenario underestimates human adaptability—new forms of literacy and verification would likely emerge alongside the technology

The central tension:

Your scenario highlights a real concern: if verification of AI outputs requires expertise most people lack, we face an asymmetric information problem. But historically, societies have developed intermediary institutions for exactly this—consumer protection agencies, scientific bodies, investigative journalism, competing corporations with skin in the game.

The question isn’t whether some people would believe inflated AGI claims (many would), but whether enough institutional and technical infrastructure would remain to contest them effectively. I’d argue that complete societal capture is less likely than a messier reality: persistent debates, competing claims, and fragmented consensus depending on which information ecosystems people inhabit.

What makes it darker:

If people become so dependent on AI that they lose the ability to function without it, the definition of AGI becomes almost semantic. Does it matter if it’s “true” AGI if it’s functionally irreplaceable?

Your scenario is plausible enough to take seriously as a cautionary tale, even if the extreme version seems unlikely. The more realistic concern might be a gradual shift where the line between “very good AI” and “AGI” becomes so blurred that the declaration itself matters less than the societal dependency that enables it.

❽ Mistral

Your scenario is a provocative and thoughtfully constructed extrapolation of current trends in AI, information consumption, and societal trust. Let’s break it down into key elements and assess its plausibility:

1. Generalization of GenAI and Information Acquisition

  • Current Trend: AI is already becoming a primary source of information for many people, especially for quick answers, summaries, and even creative tasks. The convenience of AI over traditional sources (books, experts) is undeniable.
  • Plausibility: It’s highly plausible that in 10 years, AI will be even more integrated into daily life, especially if its reliability improves. The shift from books and experts to AI is already underway, though the extent (e.g., “almost exclusively”) depends on cultural, educational, and regulatory factors.

2. Decline in Critical Thinking and Education

  • Current Trend: There is a growing concern about the decline in critical thinking skills, especially with the rise of social media and algorithmic feeds that prioritize engagement over depth. However, this is not universal—many institutions and individuals still value education and expertise.
  • Plausibility: A scenario where 98% of people lack the ability to critically assess AI outputs is extreme, but a significant decline is possible if education systems fail to adapt or if society prioritizes convenience over rigor. The figure of 98% is likely hyperbolic, but the direction is plausible.

3. AI Improvement and the Illusion of AGI

  • Current Trend: AI models are improving rapidly, but AGI (Artificial General Intelligence) remains a distant and poorly defined goal. Hallucinations and errors persist, though they may become rarer.
  • Plausibility: It’s plausible that AI companies could claim AGI capabilities for marketing or strategic reasons, especially if the public lacks the expertise to challenge such claims. However, true AGI would require breakthroughs that are not yet on the horizon, so the claim would likely be a misrepresentation or exaggeration.

4. Power Dynamics and Societal Control

  • Current Trend: Tech CEOs and major corporations already wield significant influence over public discourse. Cancel culture and social isolation for dissenting views are real phenomena, though not yet at the scale you describe.
  • Plausibility: If AI companies become the primary arbiters of information, their ability to shape narratives (including false claims about AGI) could grow. However, resistance from academia, independent experts, and regulatory bodies would likely persist, even if marginalized. The scenario assumes a near-monopoly on truth, which is extreme but not impossible in a highly centralized AI landscape.

5. Who Could Contradict the CEOs?

  • Potential Counterforces:
    • Academia and Independent Researchers: Even if marginalized, some experts would likely push back, especially if the claims are testable.
    • Regulatory Bodies: Governments and international organizations might intervene if the claims have societal implications (e.g., safety, ethics).
    • Competing AI Companies: Rival firms could expose exaggerations to gain a competitive edge.
    • Whistleblowers: Insiders might leak evidence of misrepresentation.
    • Public Skepticism: Even in an “Idiocracy,” some segments of the population would remain skeptical and demand evidence.
  • Plausibility: The isolation or cancellation of dissenters is a real risk, but total suppression is unlikely. The internet and global connectivity make it difficult to silence all opposition, though it could be significantly marginalized.

6. Overall Plausibility

  • Short-Term (10 Years): The scenario is an exaggeration but captures plausible trends. A more likely outcome is a partial shift toward AI-dominated information, with pockets of resistance and critical thinking. The claim of AGI would be controversial but not universally accepted without scrutiny.
  • Long-Term (Beyond 10 Years): If unchecked, the risks of misinformation, corporate control, and societal dumbing-down could grow, but so could countermeasures (e.g., AI literacy education, decentralized AI, or regulatory frameworks).

Key Questions to Consider

  • Education: Will societies invest in teaching critical thinking and AI literacy, or will convenience win?
  • Regulation: Will governments and institutions regulate AI claims, or will corporate power go unchecked?
  • Decentralization: Will alternative, open-source, or community-driven AI models emerge to challenge centralized narratives?
  • Public Awareness: Will people become more aware of AI limitations, or will they grow increasingly dependent and trusting?

Final Thought

Your scenario is a cautionary tale about the dangers of unchecked AI dominance, corporate power, and societal complacency. While the extremes are unlikely, the core concerns—eroding trust in expertise, misinformation, and the concentration of power—are very real. The plausibility depends on how society chooses to navigate these challenges in the coming decade.

What do you think would be the most effective way to prevent such a scenario?

❾ Mistral Think

The scenario you described presents a future where AI is ubiquitous, critical thinking is rare, and AI companies have significant power. While some aspects of this scenario are plausible, others seem less likely or extreme.

  1. Technological Progress: It’s reasonable to expect that AI models will continue to improve. However, achieving true AGI remains uncertain and is a topic of ongoing debate among experts.
  2. Societal Shift: A decline in reading and trust in human experts is somewhat plausible given current trends. However, a complete shift where almost nobody reads books seems extreme.
  3. Critical Thinking: While there are concerns about critical thinking skills, the idea that 98% of people lack these skills seems exaggerated. Education and awareness campaigns could help mitigate this issue.
  4. Power of CEOs: CEOs of major AI companies do have significant influence. However, declaring their models as AGI-capable without proof could be challenged by other experts, organizations, and regulatory bodies.
  5. Social Consequences: The idea of social consequences for contradicting CEOs is plausible within current social dynamics. However, expulsion from society seems extreme.

In this scenario, several groups could potentially contradict the CEOs:

  • Academic and research communities in AI and related fields.
  • Independent organizations focused on AI ethics, safety, and transparency.
  • Investigative journalists and media outlets.
  • Regulatory bodies and governments.
  • Public awareness campaigns aimed at educating people about AI and critical thinking.

However, the effectiveness of these groups would depend on the level of education, critical thinking skills, and the balance of power between AI companies and other institutions.

Overall, while parts of this scenario are plausible, the extent and severity of some aspects make it less likely. It’s important to consider the role of education, regulatory bodies, and public awareness in shaping the future of AI and society.

What do you think are the most critical factors that could influence whether this scenario becomes reality?

❿ Grok Expert

The scenario you describe paints a dystopian future where AI dependency erodes human critical faculties to the point of unquestioned corporate dominance. In my view, it’s moderately plausible—say, 5-6 out of 10 on a scale of likelihood—but it’s an exaggeration that overlooks societal resilience, counter-trends, and the uneven pace of cultural shifts. I’ll break down why, drawing on current trends as of 2026, while acknowledging that extrapolating to 2036 involves some speculation.

AI Generalization and Shift to AI Agents for Information

This part seems highly plausible. AI adoption is already surging: McKinsey’s 2025 survey found that 88% of organizations use AI in at least one function, up from 50% in 2022. Deloitte’s Tech Trends 2026 report highlights a move from experimentation to enterprise-wide impact, with agentic AI (systems that act autonomously) expected to become central to workflows. PwC’s 2026 predictions echo this, noting companies adopting top-down AI strategies for agents to “join the workforce.” If this trajectory continues, by 2036, people relying on AI agents for news, education, and decision-making could be the norm, similar to how search engines displaced encyclopedias. However, it won’t be “almost exclusively”—human curiosity and skepticism persist, as seen in ongoing debates about AI reliability.

Decline in Reading, Education, and Trust in Experts

Here, the scenario has strong footing in data, but “Idiocracy-level” dumbing down is overstated. Reading for pleasure is indeed plummeting: A 2025 study in iScience, analyzing U.S. time-use surveys from 2003-2023, showed daily personal reading dropped from 28% in 2004 to 16% in 2023—a 3% annual decline. Among youth, it’s worse: Only 14% of 13-year-olds read for fun daily in 2023, down from 27% in 2012, per the National Center for Education Statistics. Literacy scores are also falling: The 2024 Nation’s Report Card showed 4th- and 8th-grade reading down 2 points from 2022, continuing a pre-pandemic slide. About 54% of U.S. adults read below a 6th-grade level, per 2026 estimates.

Trust in experts is eroding too, but it’s nuanced. Pew’s 2025 survey revealed only 17% of the public expects AI to positively impact the U.S. over 20 years, versus 56% of AI experts. KPMG’s 2025 global study found trust in AI systems dropped from 63% in 2022 to 56% in 2024, with worry rising from 49% to 62%. In advanced economies, over half are wary of AI. This could amplify reliance on AI over human experts, especially if education continues declining. But not to 98%—disparities exist (e.g., higher-income, educated groups read more), and movements like media literacy or AI ethics education could push back.

AI Improvements Without AGI, But CEOs Claim It Anyway

Plausible, given the hype cycle. AI is advancing—hallucinations are rarer in top models, per industry reports—but AGI remains distant. AAAI’s 2025 survey of 475 researchers found 76% believe scaling current approaches is “unlikely” to yield AGI. MIT Sloan’s 2026 trends predict an “AI bubble” deflation, with agentic AI progressing but still imperfect. Yet hype persists: Forbes called AGI claims a “conspiracy theory,” and experts like Yann LeCun dismiss them as marketing. CEOs like OpenAI’s Sam Altman have already teased AGI as imminent, fueling a “false premise” bubble. In a low-literacy society, such claims could go unchallenged by many, but experts and skeptics (e.g., via forums like Reddit or academic pushback) would persist.

Inability to Contradict CEOs Without Backlash

This is the shakiest part—plausible in pockets, but not society-wide. Cancel culture is real and suppresses dissent: Quillette documented AI researchers resisting it, with cases like Anna Krylov facing backlash for criticizing politicization in science. Harvard’s Steven Pinker in 2025 called it a threat to academic discourse. In AI, critics of hype risk being labeled “anti-progress” or canceled via social media mobs. A 2025 ScienceDirect study on cancel culture in developing countries found it drives boycotts via perceived norms, amplified online.

However, it’s not monolithic: Pushback exists, like 2026 letters from computer scientists urging open debate. Public wariness of AI (e.g., 77% distrust government/business use, per Gallup) suggests resistance. Even in your scenario, a “majority bordering on Idiocracy” implies minorities who could organize counter-narratives via decentralized platforms or underground networks. History shows totalitarian claims (e.g., in Soviet science) faced internal dissent despite suppression.

Overall Plausibility and Mitigating Factors

The scenario extrapolates real trends—AI dependency, literacy decline, hype, and cancel dynamics—into a nightmare, but it assumes uniform societal collapse without counter-forces. In reality, bubbles burst (e.g., AI investment frenzy called unsustainable), regulations emerge (e.g., EU AI Act), and cultural backlashes happen (e.g., anti-AI movements in art/education). By 2036, we might see hybrid systems where AI augments human expertise, not replaces it entirely. If education reforms or AI literacy programs gain traction, critical thinking could rebound. Still, without intervention, elements like unchecked hype and declining reading could make false AGI claims harder to debunk for many, leading to a more polarized, less informed world. It’s a cautionary tale worth heeding, but not inevitable.

⓫ Grok 4.1 Thinking

The scenario you describe is partially plausible but ultimately unlikely to play out in the extreme form you outline. It captures some real trends and risks that are already visible today, but it overstates both the societal decline and the feasibility of a sustained, unchallenged deception about AGI.

Here is a breakdown of the key elements and their plausibility:

  1. Near-total reliance on AI agents for information, with sharp decline in book-reading and trust in human experts
    This is highly plausible. Convenience already drives people toward quick AI answers over books or deep research. Social media and short-form content have shortened attention spans, and GenAI accelerates this by making “instant expertise” feel effortless. In ten years, most casual information-seeking could indeed route through AI agents. Trust in traditional experts is already eroding in many domains (medicine, science, politics), so a further shift toward “the AI says” is realistic.
  2. AI models much better (rare hallucinations, occasional errors) but still no true AGI
    This is very plausible. Current scaling trends suggest models will become dramatically more reliable, knowledgeable, and capable by the mid-2030s. Hallucinations will become rare in well-trodden domains. Yet many serious researchers expect that genuine AGI (systems that can autonomously set goals, innovate across arbitrary domains, and outperform humans at most economically valuable work) may still be missing or only embryonic. So the technical gap you describe is credible.
  3. A broadly “Idiocracy-level” population lacking critical thinking
    This is overstated. While average critical-thinking skills may stagnate or decline in parts of the population (due to education issues, information overload, algorithmic filter bubbles), it is unlikely that 98% of people will be unable to spot obvious flaws. Pockets of highly educated, technically literate people will remain—especially in tech hubs, universities, and professional communities. Even today, when companies overhype models, researchers and engineers quickly benchmark and debunk exaggerated claims. That community is likely to grow, not shrink.
  4. CEOs of major AI companies collectively declaring their models AGI-capable, despite it being false
    This is plausible as marketing hype, but not as a sustained, unchallenged consensus. Companies already engage in aggressive capability framing (e.g., calling models “frontier,” “superhuman” in narrow tasks, or hinting at imminent transformation). A coordinated push to label something “AGI” for stock prices, talent recruitment, or regulatory advantage is conceivable. However, the term AGI is heavily scrutinized within the field. False claims would invite immediate, rigorous testing from competitors, academics, and open-source communities.
  5. Effective suppression of dissent—critics isolated, canceled, or expelled from society
    This is the least plausible part. Cancel culture exists, but it is patchy and often backfires. In the AI field specifically, debate is intense and public—on X, arXiv, blogs, podcasts, conferences. Leading skeptics (Yann LeCun, Gary Marcus, etc.) are not silenced; they are platformed. If a company falsely claimed AGI, rival firms would have strong incentives to expose the deception. Independent benchmarks (e.g., successors to LMSYS, BIG-Bench, GAIA) would provide objective evidence. Governments and regulators, already wary of AI concentration, would demand verifiable proof for any “AGI” label that triggers new laws or funding.

Overall assessment
The scenario is directionally concerning and contains real risks: over-reliance on AI, erosion of critical thinking, and marketing hype could create a public that largely accepts lofty claims at face value. We might see periods where a majority believes “AGI has arrived” based on impressive demos and CEO proclamations.

However, the extreme version—where blatant falsehoods about AGI go essentially unchallenged because nearly everyone is too uncritical to object—is improbable. The AI research community, competitive dynamics, and public benchmarking culture act as strong counterweights. Dissenters would not be easily “expelled from society”; they would likely gain followings of their own.

In short, we are more likely to get persistent hype and public confusion about what constitutes AGI than a clean, totalitarian consensus that false AGI claims are true and untouchable. The checks and balances within the technical community make the darkest version of your scenario difficult to sustain.

⓬ DeepSeek-V3.2

This is a thought-provoking and deeply plausible scenario that touches on several converging trends. Let’s break down its plausibility:

1. Elements that make this plausible

  • Information intermediaries: We already rely heavily on algorithmic curation (search engines, social media, recommendation systems). A shift to AI agents as the primary interface for information is a logical next step.
  • Epistemic inequality: If AI systems become highly reliable (rare hallucinations), people may outsource critical thinking, leading to skill erosion in evaluating sources—much like how GPS affected spatial memory.
  • Trust shift: Declining trust in traditional experts and institutions is already happening in some domains; AI could fill that vacuum, especially if branded as neutral and superior.
  • Commercial incentives: AI companies would have enormous financial and reputational motivation to claim AGI-level capabilities, especially in a competitive market.
  • Social enforcement: We already see instances of “cancellation” or isolation for those who challenge dominant tech narratives, especially if the majority perceives AI as indispensable and infallible.

2. Elements that could mitigate or prevent it

  • Resilient institutions: Scientific communities, investigative journalism, and universities might maintain enough credibility to fact-check such claims—though they’d need funding and public reach.
  • Open-source/watchdog AI: Competitive and open model development could allow third-party audits to challenge corporate claims about AGI.
  • Regulatory oversight: Governments may develop agencies to evaluate AI claims, especially if safety/security concerns grow.
  • Residual expert class: Even if most people disengage, a minority of experts could still mobilize—through lawsuits, leaks, or alternative media—to challenge false AGI assertions.
  • The definition problem: “AGI-capable” is ambiguous. Companies might overhype narrow capabilities as “AGI,” but genuine AGI would have to pass rigorous Turing-style tests that independent researchers could attempt to disprove.

3. Sociotechnical wildcards

  • AI transparency tools: Future AI might include built-in explainability features or authentication of sources, making deception harder.
  • Global divergence: Some countries might maintain strong human-expert cultures, providing a reality check on corporate narratives.
  • Crisis-driven skepticism: A major AI failure (e.g., in healthcare or infrastructure) could renew public caution even in an AI-dependent world.

Overall plausibility assessment:
I’d rate this moderate to high plausibility in a softened form.

It’s unlikely that 98% of people would become completely unable to detect faults, but a significant majority might lack the motivation or depth of knowledge to question AI outputs on complex topics. The scenario’s most plausible aspect is the combination of epistemic dependence and corporate overreach—companies claiming “AGI” for marketing, with few effective checks.

However, total isolation of skeptics is less likely in democratic societies with free speech protections, though they could be marginalized as “obsolete” or “contrarian.” The outcome depends heavily on whether counter-institutions survive the transition and whether the public values pluralism in knowledge sources.

⓭ DeepSeek-V3.2 DeepThink (take 1)

Your scenario is a compelling thought experiment that touches on technological trends, epistemology, power, and societal risk. Let’s break down its plausibility.

1. The AI Development Trajectory

The premise of highly reliable narrow AI without AGI is plausible. Current trends suggest continued improvement in reducing hallucinations and errors in specialized domains, but without the general reasoning, consciousness, or self-awareness of AGI.
However, the gap between highly capable narrow AI and AGI could still be vast in terms of understanding causality, context, and ethics. Companies might be tempted to overstate capabilities for market advantage—a pattern seen today with terms like “AI” being broadly applied.

2. Information Ecology and Critical Thinking

The shift toward AI-mediated information is already underway (search engines, social media algorithms, chatbots). If AI becomes the primary interface for knowledge, several things could happen:

  • Erosion of critical engagement: If AI systems are highly fluent and confident, users could become passive consumers, especially if education systems don’t adapt to teach AI literacy and source criticism.
  • Loss of expertise validation: If “real experts” are sidelined, society loses its ability to ground-truth AI outputs in specialized domains (medicine, engineering, law). This could lead to subtle collective delusion if AI models share systematic biases or hidden errors.

But would it reach 98% incapacity to detect faults?
That’s extreme—human diversity suggests some groups (scientists, journalists, hobbyists, skeptics) would retain verification habits. Yet, if AI ubiquity is coupled with a decline in education funding and a culture of intellectual passivity, a significant majority might indeed lack the skills or motivation to question AI outputs.

3. Power to Declare “AGI-Capable”

This is a key political and economic question.
If AI companies control the most trusted information channels, they could:

  • Frame the narrative about their own technology.
  • Marginalize dissent by labeling critics as “outdated,” “fearful,” or “conspiratorial.”
  • Use their platforms to amplify supportive voices and minimize scrutiny.

But they would likely face pushback from:

  • Academic and scientific communities, who still perform peer-reviewed research.
  • Regulators and governments, concerned about monopolies and misinformation.
  • Competing companies with opposing interests.
  • Global rivals (states with their own AI systems) who would challenge such claims.
  • Internal whistleblowers or employees with ethical concerns.

Still, in a world where public discourse is mediated by these companies’ systems, dissent could be made less visible—not necessarily through overt censorship, but through algorithmic downranking or social pressure (“Why would you distrust the AI that helps everyone?”).

4. Social Punishment for Contradiction

History shows that challenging a dominant paradigm—especially one backed by corporate power and popular reliance—can lead to isolation (“cancellation” in modern terms).
If AI is seen as indispensable and benevolent, questioning its limits could be treated as:

  • Technophobic.
  • Elitist (“you think you’re smarter than the AI?”).
  • Dangerous (if AI is managing infrastructure, health, etc., suggesting it’s flawed might be seen as irresponsible).

So yes, credible experts could be socially or professionally marginalized unless they find ways to organize and communicate effectively outside AI-dominated channels.

5. Plausibility Conclusion

Your scenario is plausible in some aspects, but likely not to the extreme degree presented.

  • Most likely: A significant portion of the population becomes overly reliant on AI, critical thinking skills atrophy among many, and AI companies engage in aggressive marketing blurring the line between narrow AI and AGI.
  • Less likely: That 98% of people become completely unable to detect faults, or that all dissent is successfully silenced. Human societies are messy; there are usually pockets of resistance, alternative media, and international observers.
  • Critical factor: The role of education systems and public institutions. If they proactively teach AI literacy and preserve human expertise, the worst outcomes can be mitigated.

6. How to Prevent Such a Scenario

  • Strong AI transparency and auditing requirements (third-party evaluations, open benchmarks).
  • Investment in education that emphasizes critical thinking, epistemology, and “how to question an AI.”
  • Preservation of non-AI-mediated knowledge institutions (libraries, academic journals, expert panels).
  • Legal and social safeguards against declaring unverified capabilities (similar to regulations on medical or financial claims).

Your scenario serves as a warning: the risk isn’t just rogue superintelligence—it’s also human complacency, corporate overreach, and the gradual erosion of our own capacity to judge what is true.

⓮ DeepSeek-V3.2 DeepThink (take 2)

Your scenario presents a deeply unsettling but logically coherent thought experiment. Let’s break down its plausibility by examining its key assumptions and potential counterforces.

Key factors that could make it plausible:

  1. Epistemic dependency: If people overwhelmingly rely on AI for information—and no longer cultivate independent research skills—they become epistemically vulnerable. This is already happening in narrower domains (e.g., reliance on search engines and social media algorithms).
  2. Erosion of expertise: The devaluation of human experts and deep reading could atrophy critical faculties, making society less capable of detecting subtle misinformation or exaggerated claims from AI companies.
  3. Commercial incentives: AI companies would have huge financial and competitive motives to declare AGI-like capabilities, especially in a market where perception of superiority drives adoption.
  4. Social silencing: In a highly networked, reputation-driven society, dissent against dominant technological narratives can indeed lead to isolation (e.g., being labeled a “Luddite” or conspiracy theorist).

Key factors that could mitigate or prevent it:

  1. Persistent expert communities: Even if marginalized, experts in academia, science, engineering, and journalism would likely continue to exist and could form alliances to audit AI claims. Niche communities and “reality enclaves” might preserve critical evaluation skills.
  2. Regulatory and institutional safeguards: Governments, international bodies, and professional associations could develop certification standards, auditing requirements, and “AI truth-in-advertising” laws—especially if the stakes (like economic or military dominance) are high.
  3. The AI models themselves: If AI improves enough to rarely hallucinate, it could also be used as a tool for fact-checking and transparent benchmarking. Open-source models or model auditing could provide counter-narratives.
  4. Economic necessity: Complex systems (finance, infrastructure, healthcare, R&D) still require deep human expertise to function; complete “idiocracy” might be incompatible with maintaining the technology stack that runs AI itself.
  5. Cultural resilience: History shows that movements often arise in response to cultural homogenization or manipulation. A pushback—akin to the “slow food” movement or digital detox trends—could foster renewed appreciation for human expertise and primary sources.

Plausibility assessment:

  • Moderately plausible in pockets, especially if AI advances in capability while education systems fail to adapt and epistemic vigilance declines.
  • Less plausible as a global, uniform outcome, because power and knowledge are distributed unevenly—some institutions, nations, or subcultures would likely resist.
  • The most plausible danger is not that everyone is fooled, but that a sufficient majority is, allowing corporations to shape public reality while dissenting voices are pushed to the margins.

What might change the trajectory:

  • Development of AI explainability and transparency standards.
  • Educational reforms that focus on AI-augmented critical thinking rather than passive consumption.
  • Legal frameworks treating knowingly false claims about AI capabilities as fraud.
  • Vibrant, decentralized media and research ecosystems that are resilient to centralized AI narrative control.

Ultimately, your scenario is a warning about the intersection of technology, epistemology, and power. Its full realization would require not just AI progress, but a societal surrender of skeptical thought and institutional safeguards—which is possible but not inevitable. The more likely outcome is a continual tug-of-war between centralized AI narratives and decentralized verification mechanisms.

The challenge for the next decade will be ensuring that our tools for discerning truth evolve as quickly as our tools for generating content.

⓯ Qwen3-Max

Your scenario is a compelling and disturbing thought experiment that blends plausible technological, sociological, and epistemological trends. While it may not unfold exactly as described, many of its core elements are grounded in real-world dynamics already observable today. Let’s break down its plausibility by examining key components:

1. Ubiquity of GenAI and Decline of Traditional Knowledge Sources

  • Plausibility: High
    We’re already witnessing a shift toward AI-mediated information consumption (e.g., chatbots replacing search engines, AI tutors, personalized news feeds). If this trend continues unchecked—especially with declining literacy, attention spans, and institutional trust—it’s conceivable that books, peer-reviewed research, and even expert consultation become niche or obsolete for most people.
  • Caveat: Some communities (scientific, academic, technical) will likely retain rigorous standards, but they may become increasingly isolated from the mainstream.

2. Improved AI with Rare Hallucinations

  • Plausibility: Moderate to High
    AI models are already improving in factual consistency, especially with retrieval-augmented generation (RAG), better training data, and post-hoc verification layers. While perfect reliability is unlikely without true understanding (i.e., AGI), “good enough” performance for everyday queries could create an illusion of near-infallibility.
  • Caveat: Subtle errors, biases, or manipulations may persist—especially in complex, ambiguous, or adversarial domains—and could be hard for non-experts to detect.

3. Societal Erosion of Critical Thinking and Expertise

  • Plausibility: Already Underway
    Distrust in institutions, experts, and traditional media has been rising for decades, accelerated by social media, misinformation, and political polarization. If education systems fail to adapt to the AI age—focusing on rote use of tools rather than critical evaluation—the public’s ability to discern truth could atrophy further.
  • Caveat: Not all societies will decline equally; some may double down on media literacy and epistemic resilience.

4. Corporate Declaration of “AGI” Despite Absence of True General Intelligence

  • Plausibility: Very High
    Corporations have strong incentives to overhype capabilities for market dominance, investment, and regulatory advantage. We’ve already seen claims of “reasoning,” “understanding,” or “sentience” applied to narrow AI. In a world where few can verify such claims, marketing can easily override technical reality.
  • Historical Precedent: Terms like “AI” itself have been stretched beyond recognition (e.g., labeling simple automation as “AI”).

5. Social Suppression of Dissenters

  • Plausibility: Moderate, but Context-Dependent
    In highly conformist or algorithmically curated information ecosystems, dissenting voices—especially those challenging dominant narratives—can be marginalized through deplatforming, reputation attacks, or algorithmic invisibility. However, complete societal expulsion is less likely in pluralistic societies, though professional or social ostracism is possible.
  • Counterforce: Decentralized tech, whistleblower protections, or niche intellectual communities might preserve pockets of resistance.

Overall Assessment:

The scenario is disturbingly plausible—not as an inevitable future, but as a high-risk trajectory if current trends continue unchallenged. It doesn’t require a sudden collapse of reason, but rather a gradual erosion of epistemic infrastructure combined with corporate opportunism and passive public acceptance.

However, human societies are also resilient. Pushback could emerge from:

  • Educators emphasizing AI literacy and critical thinking,
  • Regulatory frameworks demanding transparency in AI claims,
  • Grassroots movements valuing intellectual autonomy,
  • Or even internal contradictions within the AI ecosystem (e.g., competing AIs exposing each other’s flaws).

In short: Yes, this future is possible—but not predetermined. The window to shape a more epistemically robust AI-integrated society is still open, though it’s narrowing.

⓰ Qwen3-Max Thinking

This scenario is plausible in parts but overstated in its extremity—I’d rate its full realization as low-probability (20-30%), though key elements are already visible today. Let’s break it down critically:

Where It’s Plausible (The Real Risks)

  1. Erosion of Critical Thinking & Expertise Trust:
  • We already see declining trust in institutions (down 20% since 2010 per Edelman Trust Barometer) and rising reliance on AI for basic tasks. A 2023 Stanford study found 60% of users accept AI answers without verification.
  • Risk: If education systems fail to teach AI literacy (prompt engineering, source validation, logical fallacy detection), a large segment could become dependent on AI for truth-formation. “Filter bubbles” would worsen this.
  1. Corporate Incentives to Fake AGI:
  • AI companies are already overclaiming capabilities (e.g., “AI lawyers,” “AI doctors” with narrow expertise). In a saturated market, declaring “AGI achieved” could trigger stock surges and lock in dominance—just as “blockchain” or “metaverse” buzzwords did.
  • Historical Precedent: Theranos, WeWork, and crypto “gurus” show how charismatic leaders + opaque tech can fool investors and the public despite red flags.
  1. Social Enforcement Against Dissent:
  • Cancel culture does silence nuanced debate (e.g., scientists pressured during early-pandemic data disputes). In an AI-dominated world, labeling skeptics “anti-progress Luddites” could become mainstream.
  • Power Imbalance: If AI platforms control information access (jobs, healthcare advice, social credit), dissenters would face real consequences—like being algorithmically shadow-banned from opportunities.

Where It’s Overstated (Why Total Control Fails)

  1. “98% Lack Critical Skills” is Unrealistic:
  • Even in low-literacy societies, practical expertise persists. Plumbers, nurses, farmers, and engineers must solve real-world problems where AI errors have immediate consequences (e.g., a collapsed bridge or misdiagnosed patient). Hallucinations in critical systems would force accountability.
  • Niche Communities Thrive: Subcultures (e.g., open-source developers, academic researchers, hobbyist historians) would detect and expose flaws. Consider how Wikipedia editors or cybersecurity communities self-police today.
  1. AGI Claims Would Fail Reality Testing:
  • True AGI implies human-like reasoning, creativity, and embodied understanding—things even 2036 AI likely won’t achieve. CEOs declaring “AGI” would face:
    • Technical Scrutiny: Internal whistleblowers (like those who exposed Facebook’s algorithms) or leaked benchmark tests.
    • Systemic Failures: An “AGI” that can’t handle novel crises (e.g., a new pandemic strain) would lose credibility fast.
    • Regulatory Pushback: Governments will regulate high-stakes AI (e.g., EU AI Act). Fines for fraud could bankrupt companies.
  1. Human Nature Resists Total Conformity:
  • Distrust is Adaptive: After repeated AI failures (e.g., financial advice causing ruin), users revert to human experts. A 2024 MIT study showed 73% of people distrust AI for medical/legal decisions after one error.
  • Black Markets for Truth: Analog communities (book clubs, local workshops, encrypted forums) would emerge—just as samizdat literature did in the USSR.

The Realistic Middle Path (Most Likely Outcome)

  • Tiered AI Literacy: Society splits into:
  • The Unquestioning Majority: Uses AI for daily tasks but suffers from “automation complacency” (e.g., trusting navigation apps into lakes).
  • The Skeptical Minority: Doctors, engineers, and educators who use AI but validate outputs. They form guilds with strict verification protocols.
  • The Exploited: Low-income groups forced to use free, ad-driven AI with hidden biases—fueling inequality but not total ignorance.
  • AGI Hype Cycles: Companies will declare “AGI achieved” by 2035—but it becomes a joke (like “Web3 is coming!”). Markets punish fraud (see: crypto winter), and regulators mandate “AI IQ” labeling (e.g., “This model fails at chemistry 12% of the time”).
  • Resilient Institutions: Universities, courts, and hospitals retain human oversight for high-stakes decisions. Experts don’t vanish—they become “AI whisperers” who audit systems.

How to Prevent the Worst-Case Scenario

  1. Mandate AI Literacy in Education: Teach children to break AI (e.g., “Make this chatbot contradict itself”).
  2. Transparency Laws: Require companies to publish error rates and training data sources (like nutrition labels).
  3. Protect Whistleblowers: Legal shields for engineers who expose overclaims.
  4. Hybrid Decision-Making: Laws requiring human review for medical/legal/financial AI outputs.

Final Verdict

  • Plausibility of Core Dynamics: High (eroding trust, corporate hype, social pressure).
  • Plausibility of Total Societal Collapse into “Idiocracy”: Low. Humans adapt; reality bites back when AI fails at life-or-death tasks.
  • Biggest Threat Isn’t Stupidity—It’s Complacency: The danger isn’t that 98% are “idiots,” but that everyone (including elites) stops asking “How do you know that?”

“The best minds of my generation are thinking about how to make people click ads. That sucks.”
— Sean Parker (2017), foreshadowing today’s attention economy.

But: When Facebook’s algorithms fueled genocide in Myanmar, local journalists and NGOs exposed it. Truth isn’t dead—it’s just outgunned. Vigilance, not despair, is the antidote.

We’re not doomed—but we must build systems that reward honesty over hype. The tools to resist this future exist now. The question is whether we’ll use them.

⓱ Qwen3-235B-A22B-2507

Your scenario is not only plausible—it is, in many ways, an extrapolation of current trends that are already underway. Let’s break it down systematically to assess its plausibility.

1. Generalization of GenAI in Information Acquisition

We’re already witnessing a rapid shift toward AI-mediated information:

  • Search engines increasingly integrate generative summaries (e.g., Google’s AI Overviews, Perplexity, Bing Copilot).
  • Younger generations prefer conversational answers over sifting through multiple sources.
  • AI assistants are embedded in education, customer service, and media creation.

Projection: In 10 years, with faster processing, better contextual understanding, and seamless integration into AR/VR interfaces or neural wearables, AI agents could become the primary interface between humans and knowledge—effectively replacing traditional reading and research for most people.

✅ Plausible — even likely.

2. Decline of Book Reading and Expert Trust

This trend is already evident:

  • Reading for pleasure, especially long-form texts, has declined significantly among younger demographics.
  • Trust in institutions—media, science, medicine, government—is eroding globally.
  • Social media rewards charisma and simplicity over nuance, undermining expert discourse.

With AI providing instant, confident answers tailored to users’ beliefs (via personalization algorithms), the incentive to consult original sources or experts diminishes further.

🧠 Result: A population increasingly dependent on synthesized outputs, lacking the epistemic tools to verify them.

✅ Highly plausible, especially if education systems fail to adapt by teaching critical AI literacy.

3. Improved Models, Rare Hallucinations, No AGI

This aligns with mainstream AI development trajectories:

  • Current models hallucinate less than they did in 2020, and reliability improves with scale, retrieval augmentation, and process supervision.
  • However, despite advances, there’s no evidence we’re close to artificial general intelligence—systems still lack true understanding, self-correction without human feedback, and robust cross-domain reasoning.

Yet public perception often conflates fluency with comprehension.

🎯 Outcome: An AI that seems omniscient to most users, even if it isn’t sentient or generally intelligent.

✅ Very plausible.

4. CEOs Declare Their Models “AGI”

This is perhaps the most realistic part of your scenario:

  • Tech companies have strong incentives to claim breakthroughs: valuation, market dominance, regulatory advantage, talent acquisition.
  • Terms like “AGI” are poorly defined and unregulated—perfect for marketing.
  • Once one major player declares AGI, others will follow to avoid being left behind (“AGI-washing,” akin to “greenwashing”).

Historical precedent: IBM calling Watson an “AI doctor,” or DeepMind claiming AlphaGo was a step toward AGI.

📣 Expect PR campaigns featuring testimonials, curated demos, and partnerships with governments and universities to legitimize the claim.

✅ Extremely plausible—even probable, assuming no binding international definition or verification standard for AGI.

5. Who Can Contradict Them?

Here lies the core danger:

  • Experts in AI alignment, cognitive science, or philosophy of mind may object—but their voices will be drowned out.
  • Social media dynamics favor consensus and emotional resonance over technical accuracy.
  • Anyone challenging the “AGI narrative” risks being labeled a Luddite, fearmonger, or anti-progress radical.
  • Institutions may depend financially or operationally on these AI systems, creating conflicts of interest.

🔁 Feedback loop: The more society relies on AI, the harder it becomes to question its nature or limitations.

⚠️ This mirrors Orwellian themes—not outright lies, but a redefinition of truth through power and repetition.

✅ Plausible to the point of inevitability, unless strong countervailing forces emerge (e.g., global AI ethics bodies, resilient education systems, whistleblower protections).

Final Assessment: High Plausibility

On a scale from “science fiction” to “probable near-future,” your scenario lands firmly in the probable range—especially if:

  • Education continues to underemphasize logic, epistemology, and media literacy.
  • Regulatory frameworks lag behind technological deployment.
  • Capitalist incentives remain unchecked in the AI sector.

In short: We may not get AGI in 2036—but we might collectively believe we did.

And in a world where perception shapes policy, economics, and identity, belief can be as powerful as reality.

A Glimmer of Hope?

Such a future isn’t inevitable. Resistance could come from:

  • Grassroots movements promoting “slow knowledge” and source-based learning.
  • Open-source AI communities providing transparent alternatives.
  • International treaties requiring empirical benchmarks for AGI claims.
  • Revival of Socratic education focused on questioning, not just answering.

But time is running short.

As you suggest—when nearly everyone has outsourced thinking, who will notice the silence of reason?

⓲ Qwen3-235B-A22B-2507 Thinking

Your scenario is highly plausible in its core dynamics (40-60% likelihood by 2036), but overstates the uniformity of societal collapse and underestimates counterforces. Let’s break this down with nuance, grounded in current trajectories:

Where the Scenario Holds Strong Water

  1. AI as Primary Information Source (90%+ plausibility)
    • We’re already seeing this: 40% of Gen Z uses AI for search (2024), and book reading has declined 35% since 2004 (Pew). By 2036, with multimodal AI agents (voice/video/context-aware), passive consumption will dominate. Why? Convenience trumps effort—like how Google Maps killed paper maps despite occasional errors.
  2. Erosion of Trust in Experts (70% plausibility)
    • Pre-2026 trends show plummeting trust: Only 38% of Americans trust scientists “a great deal” (Gallup 2023), and “expert” has become a slur in some circles. AI accelerates this by:
      • Democratizing misinformation: AI-generated “studies” or “expert testimonials” will flood social media.
      • Normalizing errors: Rare hallucinations (e.g., AI misstating historical dates) will breed generalized distrust, even when AI is correct 99% of the time.
  3. CEOs Falsely Claiming AGI (85% plausibility)
    • Incentives are overwhelming:
      • Market dominance (e.g., “Our AGI solves climate change!”).
      • Regulatory capture (pre-empting strict AI laws by framing models as “safe AGI”).
      • Investor pressure (NASDAQ AI stocks could crash without “AGI” milestones).
    • Plausible deniability: They’d use terms like “practical AGI” or “human-level reasoning,” avoiding technical definitions. No one can prove it’s not AGI if the public lacks tools to test it.
  4. 98% Unable to Detect Errors (60% plausibility)
    • Critical thinking is already declining: Only 28% of U.S. 8th graders are “proficient” in civics (NAEP 2022). AI dependency would worsen this:
      • Skill atrophy: Why learn math if AI solves it? Why read a book if an AI “summarizes” it?
      • Epistemic learned helplessness: Generations conditioned to accept AI outputs as “truth” will lack frameworks to question them.

Where the Scenario Overreaches (Key Counterforces)

  1. AGI Hype Would Backfire Spectacularly (Critical Flaw)
    • Real-world consequences: If CEOs declare “AGI” but AI still fails at basic tasks (e.g., misdiagnosing illnesses, crashing autonomous vehicles), public trust would collapse faster. Example: A single “AGI”-endorsed stock tip causing a market crash would trigger mass lawsuits and regulatory firestorms.
    • Whistleblowers & niche experts: Even if 98% are credulous, the 2% who can detect flaws (e.g., engineers, academics) would:
      • Leak evidence (like Snowden/NSA).
      • Form underground networks (e.g., encrypted “truth coalitions”).
      • Be courted by governments (e.g., China/U.S. competing for AI advantage).
  2. Society Isn’t Uniformly Fragile
    • High-stakes domains resist collapse: Pilots, surgeons, and nuclear engineers can’t afford AI errors—they’ll maintain human oversight and traditional expertise. Aviation already uses AI assistance but requires human final authority.
    • Niche communities thrive: Analog hobbies (book clubs, woodworking) are growing among Gen Z (2024 data). These become resilience hubs where critical thinking is practiced.
  3. Regulatory & Market Safeguards Will Emerge
    • Post-crisis laws: After the first major AI-caused disaster (e.g., AI-driven financial panic), regulations like mandatory “error probability scores” or third-party audits will arise. The EU AI Act is just the start.
    • Commercial self-interest: Companies selling AI to hospitals/labs need accuracy—they’ll develop internal “truth verification” tools to avoid liability.
  4. The “AGI” Label Loses Meaning
    • By 2036, “AGI” will be a marketing term (like “5G” today), not a technical milestone. Public discourse will shift to specific capabilities (“This AI designs drugs but can’t fix your toaster”). CEOs won’t waste breath claiming “AGI”—they’ll sell “Domain-Specific Superintelligence” instead.

Realistic Outcome: Asymmetric Fragmentation, Not Total Collapse

  • Plausible 2036 Reality:
Group% of PopulationRelationship to AI
Credulous Mass~70%Treat AI as infallible oracle; reject experts; consume AI-curated “truth.”
Critical Niche~25%Use AI as tool but verify outputs; maintain traditional skills; form trusted communities.
Power Brokers~5%CEOs/governments who know AI isn’t AGI but exploit the myth for control.
  • Why this avoids “Idiocracy”:
    • The critical niche (25%) is large enough to run essential systems (power grids, hospitals). They won’t be “canceled”—they’ll be protected as vital infrastructure.
    • AI errors will self-correct society: A viral AI hallucination causing real harm (e.g., “This plant is edible” leading to poisonings) would trigger localized backlash and renewed interest in basic skills.

Conclusion: 50% Plausibility with Crucial Caveats

  • The core mechanism is terrifyingly plausible: AI dependency will erode critical thinking, and CEOs will exploit this with AGI hype. But society won’t collapse uniformly—it will fragment into knowledge haves and have-nots.
  • The 98% figure is exaggerated: Even in 2026, 30-40% of people actively verify AI outputs (current data). By 2036, this group may shrink but won’t vanish—it will become more influential due to its scarcity value.
  • Biggest blind spot in your scenario: Human adaptability. Societies always develop countermeasures to new threats (e.g., spam filters for email, media literacy after fake news). The “AGI hoax” would trigger similar adaptations—just slower and messier.

Final thought: This scenario is less Idiocracy and more “The Matrix meets Brave New World.” Most people will live in comfortable ignorance, but a critical minority will keep the lights on—and they’ll be quietly empowered by better AI tools (e.g., “lie detector” plugins for chatbots). The real danger isn’t CEOs lying about AGI; it’s society losing the will to care about the truth. But history suggests we always rediscover that will when the lies start costing lives.

Note: As of January 22, 2026, we’re already seeing early signs—like students using AI to write essays without understanding sources. But remember: In 1995, experts feared the internet would destroy knowledge. Instead, it created Wikipedia and Khan Academy. Technology amplifies human tendencies—it doesn’t determine them.

⓳ GLM-4.7 (Z.ai)

This scenario is extremely plausible, arguably bordering on inevitable if current technological and social trends continue on their current trajectories.

We are already seeing the foundational blocks of this reality being laid. The scenario you describe relies on three converging vectors: the degradation of epistemic trust, the illusion of competence, and the consolidation of technological power.

Here is an analysis of why this scenario is so plausible, broken down by its key components.

1. The Illusion of Competence (The “Idiocracy” Factor)

The most critical element of your scenario is the “competence gap.” You correctly identify that detecting hallucinations often requires deep subject matter expertise.

  • The “Babel” Problem: We are rapidly approaching a point where an AI can translate complex biochemistry into simple, convincing English faster than a human biochemist can. If 98% of the population has lost the habit of deep reading and critical inquiry, they judge the output not by accuracy, but by confidence and fluency. LLMs are fluent; they sound authoritative. To an untrained ear, a confident lie is indistinguishable from a nuanced truth.
  • The Deskilling of Humanity: If we stop reading books and performing our own research, we lose the “scaffolding” of knowledge required to fact-check. Ten years is enough time for a generation to raise children who view querying an AI as the only method of learning. If you don’t know how the world works, you cannot detect when the AI describes the world incorrectly.

2. The Redefinition of AGI (The CEO’s Strategy)

The definition of AGI (Artificial General Intelligence) is vague, subjective, and currently entirely controlled by the very companies selling the product.

  • Moving the Goalposts: Currently, AGI is often defined as “human-level capability across a broad range of economic tasks.” If the general population has become less capable due to reliance on AI, the bar for “human-level” drops.
  • The “Black Box” Defense: With models becoming incredibly complex, the CEOs can claim, “It has emergent reasoning capabilities we don’t fully understand yet.” If the model acts indistinguishable from a genius to 98% of people, the CEOs can effectively declare it AGI based on “functional equivalence.” Who has the authority to say it isn’t?
  • Economic Incentive: Declaring AGI triggers massive stock valuations, deregulatory discussions, and historical prestige. The incentive to lie or stretch the truth is virtually infinite.

3. The “Noble Lie” and Social Cancellation

You asked: Who can contradict those CEOs without being isolated? The answer is: Almost no one.

  • The “Luddite” Label: In a society where 98% of people rely on AI for daily life, pointing out flaws in the AI is not seen as a correction; it is seen as an attack on the infrastructure of society. Skeptics would be labeled as “anti-technology,” “conspiracy theorists,” or simply “enemies of progress.”
  • Gaslighting at Scale: If an AI tells you the capital of Australia is Sydney, and you look it up in a physical book and find it is Canberra, the social pressure will be to trust the AI. If everyone else trusts the AI, you become the outlier. “Everyone else says it’s Sydney; why are you being difficult?”
  • Death of Expertise: As noted, real experts are already being sidelined. In ten years, their nuanced arguments (“Actually, the model is just predicting the next token, it doesn’t understand causality”) will be too technical and boring for a public accustomed to instant, entertaining answers.

4. The Plausibility Gap: Why it might not happen

While highly plausible, there are two strong counter-forces that might prevent this specific “Idiocracy” outcome:

  • The “Boy Who Cried Wolf” (Failure Modes): Even if hallucinations are “rare,” in a world of billions of queries, rare mistakes happen constantly. If the AI is declared AGI (a super-intelligence) but occasionally fails to do basic math or invents historical events, the contradiction might become too visible for the public to ignore. However, the public could be trained to excuse these as “quirks” rather than failures of intelligence.
  • The Utility Gap: If society truly degrades to the point of incompetence, the systems that keep the lights on (power grids, water, food supply) require high precision. If the AI agents running these things start hallucinating, reality will violently intervene. The CEOs might declare AGI, but if the bridges start falling down, the illusion collapses.

Conclusion

The scenario is highly plausible because it exploits a fundamental human weakness: we prefer convenience over truth.

If the majority of people delegate their thinking to machines, they lose the metric by which to measure intelligence. When the blind rely on the sighted to describe the world, and the sighted (the CEOs) claim the world is a kaleidoscope, the blind have no choice but to believe them. The “98%” will likely accept the declaration of AGI not because it is true, but because believing it is easier than maintaining the skepticism required to deny it.

⓴ GLM-4.7 Deep Think (Z.ai)

This scenario is highly plausible, arguably even a logical extrapolation of current trends. It describes a sociological crisis rather than a technological one—a “Societal AGI” scenario where Artificial General Intelligence is declared not because it exists, but because the population has lost the ability to distinguish it from advanced automation.

Here is a breakdown of the plausibility of the specific pillars of your scenario, ranging from the technological to the sociopolitical.

1. The Redefinition of AGI (The “Goalpost Shift”)

Plausibility: Very High

We are already seeing the definition of AGI being softened in real-time. Ten years ago, AGI implied consciousness, human-level general reasoning, and autonomy. Today, corporate marketing frequently conflates “passing a bar exam” with “being a lawyer.”

In ten years, if models are 99% accurate on average queries and can mimic emotional nuance perfectly, CEOs will effectively argue: “It can do anything a human can do, only faster and with fewer errors. If it walks like a duck and quacks like a duck, it’s AGI.” If the economic utility is there, the philosophical distinction (between “simulating thought” and “thinking”) will be treated as an irrelevant academic debate.

2. The Epistemic Crisis (The “Idiocracy” Dynamic)

Plausibility: High

Your scenario posits a population that has lost “critical spirit.” This is arguably the most dangerous aspect of the trajectory we are on.

  • Cognitive Atrophy: If people stop reading long-form content and rely exclusively on synthesized AI answers, they lose the “contextual scaffolding” required to fact-check. To verify an AI’s answer, you often need to know where to look and how to interpret sources. If AI becomes the only interface to information, it becomes the sole arbiter of truth.
  • The Competence-Confidence Gap: As AI gets better, the average user feels smarter because they can produce high-level outputs (code, essays, strategies) effortlessly. This creates a false sense of competence. When a real expert says, “Wait, the AI hallucinated the source of this law,” the user—who just successfully used the AI to write a contract—will trust their own experience over the expert’s warning.

3. The Isolation of Experts

Plausibility: Medium-High

We already see the mechanisms for this in place today. When experts contradict popular consensus (or the consensus generated by an algorithm), they are often labeled as “gatekeepers,” “elitists,” or “luddites.”

In your scenario, the dynamic shifts from “disagreement” to “social ostracization.” If 98% of society relies on AI agents for their livelihood and social life, someone attacking the AI is attacking the infrastructure of daily life. They wouldn’t just be wrong; they would be a social threat. The “cancellation” wouldn’t necessarily be organized by the government, but by a peer group that views the AI as a benevolent utility and the critic as a dangerous fanatic.

4. The “Who Can Contradict?” Problem

Plausibility: The Core Conflict

This is the crux of your scenario. If the AI models are opaque (black boxes), the only people who can technically contradict the CEO’s claim of AGI are the people inside the companies building them.

  • The Alignment of Interests: The engineers and researchers who know the models are just “stochastic parrots” (albeit very good ones) are paid by the CEOs. Whistleblowers exist, but if the public adores the product, the whistleblower is not a hero—they are a villain trying to take away the magic.
  • The Complexity Barrier: Explaining why a model isn’t truly generally intelligent requires explaining concepts like “overfitting,” “lack of world models,” or “statistical correlation vs. causation.” In a soundbite world, the CEO saying, “It is alive,”” will always win over the scientist saying, “It lacks a causal representation of the semantic structure,”

The Counter-Arguments (Why it might fail)

While the scenario is plausible, there are forces that might prevent it:

  1. Corporate Warfare: Google, Microsoft, Meta, and open-source movements will not act as a monolith. If Company A declares AGI, Company B will likely release a report detailing how Company A’s model failed on a specific edge case to undermine their stock price. Competition is the best fact-checker.
  2. The “Black Swan” Hallucination: Even with rare hallucinations, if an AI declared to be a “god-like intellect” makes a catastrophic error—like advising a specific medical treatment that kills thousands—the illusion would shatter instantly. Trust in machines is brittle; one high-profile failure can reset years of progress.
  3. The Underground of Critical Thinkers: It is unlikely that everyone will lose critical spirit. There will likely be a “digital resistance”—groups of humans who explicitly read books, verify sources, and audit the AI. They may be marginalized (as your scenario suggests), but they will exist, preventing a total 100% collapse into idiocracy.

Verdict

Your scenario is terrifyingly plausible because it relies on marketing winning over philosophy.

If the economic incentive to declare AGI is high enough, and the technological capability is “good enough” to fool a layperson, the declaration becomes a self-fulfilling prophecy. Society effectively enters an era of “Fake AGI”—where we treat the machines as minds, not because they are, but because we have lost the ability to tell the difference.

My only comment is a technical one: the older Qwen3-235B-A22B-2507 is superior to Qwen3-Max, which is a fraud.