Based on systematic web search and multi-round deep dialogue, this paper proposes a nine-layer progressive analytical framework for AI disenchantment. From the collapse of macro-narratives, community backlash, frontline human disenchantment with AI as a tool, the backfire of usage inertia against marketing narratives, the triple squeeze degrading AI from “paradigm” to “function,” the collapse of narrative premiums in capital markets, the perception ceiling and structural scissors gap from model convergence, evolutionary psychology’s hard constraints on AI sociality, to disenchantment accelerating into collective consensus through social networks — each layer builds upon the previous one. Version 2 introduces the core meta-thesis: the essence of AI disenchantment is a head-on collision between functional evolution and biological evolution. AI’s automation trajectory eliminates variables and approaches the optimal, while the human biological system depends on variables to build trust, generate incremental value, and sustain social bonds. This collision cannot be engineered away.
The Three Meanings of “Disenchantment”
First meaning: Weberian Entzauberung. The concept proposed by Max Weber in 1917 — rationalization and technological progress strip the world of its sense of mystery. In the AI context, this means AI itself as a technology is understood, dissected, and no longer viewed as “magic.” The subject is the cognitive system; the object is the technology itself.
Second meaning: Consumer Disenchantment. Ordinary users shift from blind faith in and overestimation of AI to viewing it on equal terms and reducing it to a limited tool. The subject is the end user; the object is the actual experience of AI products.
Third meaning: Capital Disenchantment (Narrative De-leveraging). Investors transition from emotionally driven investment propelled by AI’s grand narratives (especially AGI) toward demanding verifiable ROI. The subject is the capital market; the object is the valuation logic of AI companies.
The timelines, transmission mechanisms, and consequences of these three forms of disenchantment differ, but they share a common underlying driver — the human biological system’s instinctive rejection of AI’s “perfection.” The nine-layer structure of this paper unfolds precisely along the interweaving of these three meanings.
Narrative Collapse: From “Storytelling” to “Accounting”
The English-language context revolves around three keywords: disenchantment, hype fatigue, and ROI reckoning. TechCrunch declared 2026 the year AI moves from hype to pragmatism. Multiple Stanford AI experts jointly predicted that the era of AI evangelism is giving way to the era of evaluation. Pew Research data shows roughly half of American adults feel concerned rather than excited about AI, a proportion that has steadily risen from 37% in 2021.
The Chinese-language context uses the Weberian concept of “disenchantment” (祛魅) to subsume a broader cultural reckoning. 36Kr defined 2026 as “the year of disenchantment”; Tencent News cited Stanford’s predictions, stating “the era of securing billions in funding through grand narratives is over.” The National Business Daily used the OpenClaw “Lobster” as a case study — 500 yuan to install, 200 yuan to uninstall in just 10 days — arguing that what truly needs uninstalling is the “AI anxiety” in our minds.
The core consensus across both contexts: in 2026, AI is shifting from “storytelling” to “accounting,” from myth to tool.
Social Media & Online Communities: AI Slop Becomes Public Enemy
Merriam-Webster named “slop” its 2025 Word of the Year. Visibrain data shows “AI slop” was mentioned over 475,000 times within 30 days, with 28.9% expressing negative sentiment. Most notably, #supporthumanart became the most commonly associated hashtag — the anti-AI content discourse is catalyzing a renewed appreciation for human creation.
Reddit’s AI communities have evolved from hype engines into “adversarial review boards.” Users’ core demand has shifted from “wow factor” to “controllability.” Xiaohongshu (RED) required AI-generated content to carry mandatory labels starting February 2026, and fully banned AI-managed posting accounts in March. CNN predicted 2026 will be the inaugural year of “100% Human” marketing.
Human Biological Adaptation and AI’s Functional Interface
The human dopamine system has completed its “desensitization” to AI — just as we no longer marvel at touchscreens or 4G. One analysis used a party metaphor to describe this process: “The music is still playing, the lights are still on, people are still dancing, but the persuasiveness is fading. The conversations are looping.” By Q4 2026, AI’s marketing buzz will not dissipate through a crash, but through the audience quietly walking out.
AI is shifting from “the universal solution to all problems” to infrastructure — present, useful, but fundamentally running in the background. When tools integrate into ordinary workflows instead of remaining viral demo pieces, people can evaluate them more soberly. The impulse to chase the latest version weakens; attention shifts from novelty to fit.
An AI survival guide on a Chinese tech forum writes: “AI has long since shed the mythical aura it carried years ago. We no longer marvel at its ability to write poetry or create art, because it has become infrastructure like water, electricity, and gas.”
The most effective disenchanter is not the critic, but daily use itself.
How Usage Inertia Compresses AI Companies’ Narrative Space in Reverse
Layer Three revealed the human biological system’s desensitization to AI — the evaporation of novelty. But how does this individual-level desensitization translate into industry-level narrative compression? A key mechanism is missing in between: users’ actual use cases inversely define AI’s value domain, and this value domain is far smaller than what AI companies’ marketing narratives claim.
When what users do with AI every day is: translate emails, summarize documents, organize schedules, edit photos — these concrete, repetitive, limited use cases solidify AI’s position in the user’s mind like a riverbed. AI is no longer “a world-changing force” but “a tool that helps me get things done.” It is users’ usage inertia that defines what AI is, not AI companies’ slide decks.
The destructive power of this mechanism lies in the fact that when AI companies try to use new narratives (Agents, AGI, embodied intelligence) to reactivate excitement, users have already been “anchored” by their daily experience. You tell them “AI will become your digital employee,” and what they’re thinking is “it couldn’t even remember the email format I requested last week.” Embodied experience is the most honest disenchanter — it cannot be overwritten by marketing, because it refreshes every single day.
AI companies’ greatest narrative enemy is not competitors, but users’ own daily experience. Every “good enough” usage experience drags AI down from the heights of “paradigm” to the flat ground of “function.”
AI Degraded from “Paradigm” to “Function”
First squeeze: Biological adaptation. The human brain’s dopamine circuitry has completed neuroadaptive desensitization to AI; any sustained stimulus is biologically degraded to background noise.
Second squeeze: Functional demotion. AI is being “swallowed” by operating systems as a system-layer component. Dell admitted at CES 2026 that consumers don’t pay for AI features. Android Police stated bluntly that Samsung’s Galaxy AI was “nearly useless.” When AI becomes phone camera optimization and battery management, it is no longer a “product” that can sustain its own standalone narrative. By 2026, users won’t ask whether an app “has AI” — they’ll assume it does.
Third squeeze: Usage inertia devours narrative. Users defined AI’s true value domain through actual use cases, and this value domain is far smaller than the “world-changing” marketing narrative. 29% of consumers hang up immediately when forced to interact with AI on calls. When everything is “good enough,” differentiation through refinement has vanished.
AI companies’ greatest narrative enemy is not competitors, but users’ own daily experience. When users discover that AI helping them write emails and manage schedules is already “good enough,” the “world-changing” story loses its landing point.
Narrative Premium Collapse: $2 Trillion Evaporated
Bloomberg described it as a “stock market doom loop” — the market simultaneously feared two contradictory things: that AI would destroy entire industries, while also doubting that the companies building AI would ever earn back the money spent. Both fears fed each other for weeks.
JPMorgan called it “the largest non-recessionary 12-month drawdown in over 30 years.” The keyword is “non-recessionary” — this was not a decline driven by economic fundamentals, but pure narrative-valuation decoupling. Company revenues were still growing (Meta +24% YoY, Palantir +70% YoY), yet stock prices plummeted.
The underlying mechanism can be understood through a narrative dopamine decay table — each round of AI narrative requires a stronger stimulus than the last to maintain investor excitement, but the stimulus sources are running out:
| Phase | Narrative Content | Biological Stimulus Mechanism | Status |
|---|---|---|---|
| 2022–23 | “AI can write poetry!” | Novelty (dopamine peak) | Exhausted |
| 2024 | “AI will transform all industries!” | Fear + Greed (amygdala) | Fatigued |
| 2025 | “Agents are here!” | Renewed novelty | Briefly effective |
| 2026 | “AGI is coming!” | Ultimate promise | Losing efficacy |
| 2026+ | ? | No new narrative available | Narrative vacuum |
When the final card of AGI is played and goes unredeemed — Stanford HAI co-director stated bluntly “you will absolutely not see AGI in 2026” — the narrative toolbox is empty. A Goldman Sachs strategist warned by analogy to the newspaper industry: newspaper stocks fell an average of 95% between 2002 and 2009 under the impact of the internet.
AI stock valuations depend not on financial fundamentals, but on a continuous supply of narrative stimulus. When narratives lose their biological activation capacity, valuations lose their anchor. You cannot sell an upgraded version of the same stimulus to an already-desensitized brain.
Model Convergence, User Indifference, and the Structural Scissors Gap
March 2026 benchmarks show: Claude Opus 4.6, GPT-5.4, and Gemini 3.1 Pro differ by only 1–2 percentage points on major benchmarks, with each taking turns leading. API prices have dropped 40–80% year-over-year. For 90% of users, the outputs of all frontier models are already indistinguishable.
| Benchmark | Claude Opus 4.6 | GPT-5.4 | Gemini 3.1 Pro | User-Perceivable Difference |
|---|---|---|---|---|
| SWE-bench (Code) | 80.8% | 74.9% | 80.6% | Only professional developers can perceive |
| ARC-AGI-2 (Reasoning) | 68.8% | 52.9% | 77.1% | Only AI researchers care |
| GPQA (General Reasoning) | 91.3% | 92.8% | 94.3% | Ordinary users completely indifferent |
Christopher S. Penn, analyzing GPT-5, stated bluntly: “We are hitting the wall of diminishing returns for single dense models.” GPT-5 is actually a router plus sub-model combination. NeurIPS experts observed that naive scaling is hitting a “scaling wall.”
This constitutes a structural scissors gap:
MIT Project NANDA reports: 95% of enterprise generative AI projects have failed to produce measurable ROI. Meanwhile, global generative AI spending reached $644 billion. The foundation is further eroded by open source — DeepSeek V4’s API pricing is approximately 27 times cheaper than comparable closed-source models. AI companies are heading toward “telecom-ification” — infrastructure providers with low margins, high capital intensity, and pricing power held by users.
Functions are priced by cost, not by dreams. When AI shifts from “paradigm” to “function,” its valuation logic must switch from “narrative premium” to “discounted cash flow.”
The Human Rejection System: The Deepest Layer of AI Disenchantment
The formation of AI-to-AI communication closed loops. OODA Loop analysis predicts that by 2026, 90% of online content will be AI-synthesized. The communication chain has undergone three-stage degradation: Human → AI → Human reads (AI as tool), Human → AI → AI → Human (AIs competing), AI → AI → AI (humans out of the loop). Each stage loses the most essential element of human sociality — the authenticity of intent.
The human social fault-tolerance and complementarity mechanism. The core of the human social system is not information exchange efficiency, but building reciprocal relationships through tolerating each other’s imperfections. You tolerate my clumsiness → I perceive your goodwill → Trust is established. You expose your weakness → I perceive your authenticity → Intimacy is built. You make a mistake and apologize → I exercise forgiveness → Social bonds are strengthened. Each step requires both parties’ imperfections as raw material.
The Pratfall Effect’s experimental validation. Research published in Frontiers in Robotics and AI confirmed: participants’ likability ratings for flawed robots were significantly higher than for perfect ones. Berkeley’s Dietvorst et al.’s “algorithm aversion” research found that after witnessing an algorithm make even a single error, people prefer flawed human judgment. Humans are not choosing “better outcomes” — they are choosing “more trustworthy relationships.”
| Interaction Partner | When Imperfect | When Perfect |
|---|---|---|
| Human-to-Human | Fault-tolerance activates → Trust + Intimacy | Rejection system activates → Suspicion + Distance |
| Human-to-AI | Fault-tolerance activates → Likability increases | Rejection system activates → Uncanny valley + Aversion |
The 2026 market validates this mechanism. Human-written articles generate 5.44× the traffic of AI articles, with 41% longer dwell time. Instagram head Mosseri said: “In a world of AI-generated perfect content, it’s humans that are cherished.” An entire industry is being born around the proposition that “imperfection is luxury.”
Users who deploy AI for social interaction are blocked by the human rejection system. When a person uses AI as a substitute for their own social presence, others detect this “perfection” and reject not the AI, but the person who used it — because the signal transmitted is: “You’re not worth my investing real time and effort.” This is one of the most severe insults in human sociality. A 250,000-member anti-AI group specifically names and shames brands that use AI. What is being rejected is not the machine, but the person who chose to let the machine stand in for them.
The direction AI companies pursue (more accurate, more fluent, more “perfect”) runs directly counter to the direction of human psychological acceptance. Each round of model upgrades makes AI more “perfect,” and every increment of “perfection” pushes it deeper into the uncanny valley. This is not a problem solvable with more compute. It is a constraint written in human genes.
The Contagion Dynamics of Disenchantment: How Individual Collisions Accelerate into Collective Consensus
AI disenchantment is not a synchronous event, but a diffusion process. Each person experiences the “functional collision” on their own timeline — AI’s perfection triggers biological rejection. But as social animals, humans transmit this experience at high speed through social networks.
The transmission mechanism has been confirmed by research. A Harvard Business Review study from March 2026 pointed out: what drives genuine AI adoption is peer influence — when employees see trusted colleagues share AI’s successes and failures, they rapidly calibrate their own expectations. This mechanism is bidirectional: positive experiences spread adoption; negative experiences spread skepticism.
The critical asymmetry. Ferrara’s 2026 paper on the “Generative AI Paradox” published in an academic journal revealed a key mechanism: once users become aware that AI-generated content exists, skepticism extends to all digital content — including authentic content. Rational actors begin discounting all digital evidence. This means building AI trust requires long-term accumulation, while destroying AI trust requires only a single virally spread counter-example.
Instagram head Mosseri predicted users will “shift from defaulting to believing what they see is real, to starting from skepticism when encountering media.” He added that this is “extremely uncomfortable for everyone, because at a genetic level we are inclined to trust our own eyes.” Forrester’s 2026 prediction report confirmed: consumers are turning to personal social networks for guidance — trust is migrating from institutions to interpersonal networks.
The spread of disenchantment doesn’t require every person to hit the wall themselves. As long as enough key nodes in social networks transmit the signal that “AI is nothing special,” the entire network rapidly recalibrates its expectations of AI. And this recalibration is irreversible — once the cognition that “any content could be AI” takes root, skepticism becomes the default state.
First Principles: The Head-On Collision of Functional Evolution and Biological Evolution
The nine layers of analysis above can be unified by a single meta-thesis: the essence of AI disenchantment is a head-on collision between AI’s functional evolutionary trajectory and humanity’s biological evolutionary heritage.
AI’s functional evolutionary trajectory: faster, more accurate, more fluent, more consistent, more perfect — eliminating variables, approaching the optimal solution.
Humanity’s biological evolutionary heritage: depends on variables to build trust, depends on imperfection to activate fault-tolerance, depends on the unexpected to generate new knowledge, depends on inconsistency to identify conspecifics, depends on clumsiness to perceive goodwill.
These two lines are not parallel, nor do they cross and diverge — they are on a head-on collision course. Every step AI advances tramples on the most sensitive areas of the human biological system:
| Domain | AI’s Advancement Direction | Human Biological Response | Result |
|---|---|---|---|
| Social | The more human-like AI becomes | The more uncanny valley triggers | Greater rejection |
| Cognitive | The more accurate AI becomes | The more it exceeds perception thresholds | Becomes redundant |
| Emotional | The more considerate AI becomes | The more sycophancy detection triggers | Greater discomfort |
| Creative | The more perfect AI becomes | The more it eliminates variables | Cannot produce incremental value |
This collision is irreconcilable — AI companies cannot make AI “deliberately worse” to accommodate humans (that would negate the entire technological narrative and valuation foundation), and humans cannot rewrite 1.5 million years of evolutionary heritage in three years to adapt to AI’s “perfection.”
And the core promise of the AGI narrative — “AI can autonomously generate infinite incremental value” — constitutes a fundamental contradiction with this collision: how can a system designed to eliminate variables produce incremental value that depends on variables? Optimizers don’t make meaningful mistakes. But meaningful mistakes — human intuitive leaps, inadvertent path deviations — are precisely the source of new knowledge. AI’s “perfection” repels not only humans, but also “the new.”
The biggest proposition of AI disenchantment is not that AI isn’t good enough, but that “good” itself repels “new,” repels “trust,” repels “human.” Automation lacks variables — especially incremental value. And the industry narrative’s greatest false promise is that AGI will generate unlimited self-increment.
After Disenchantment: The Possible Endgames for the AI Industry
Endgame One: Telecom-ification. AI becomes infrastructure — low margins, high capital intensity, pricing power belongs to users. API prices continue to fall, open-source models cannibalize commercial share, and AI company valuations regress from the “platform monopoly profit” narrative to “utility” levels. This is the most likely near-term endgame.
Endgame Two: Imperfection by Design. If the “imperfection as luxury” trend persists, AI product design may undergo a paradigm shift — from pursuing 100% accuracy toward designing “controllable humanizing flaws.” But this directly conflicts with the industry’s benchmark-racing inertia and carries ethical risks: once deliberately designed “imperfections” are detected by users, they trigger even stronger rejection.
Endgame Three: Scientific Tool-ification. AI hits a biological ceiling on the consumer and social fronts, but its value in scientific research domains (protein folding, drug discovery, materials science) is not constrained by human perception thresholds. AI may contract from “a universal tool for everyone” to “a research accelerator for specialists,” with profits concentrating in vertical B2B domains.
Endgame Four: The Return of Human Value. The anti-AI movement is not a temporary mood swing but a systemic response from the human social fault-tolerance mechanism. “100% Human” marketing, the #supporthumanart movement, and Xiaohongshu’s AI content ban are all institutional expressions of this response. The “imperfection” of human creation will shift from “defect” to “asset” — just as handmade goods became luxury items in the industrial age.
AI won’t disappear, but its narrative premium will be compressed for the long term. The future winner is not the most “perfect” AI, but the one that best understands where to yield, where to stay silent, and where to remain clumsy — because in the collision between functional evolution and biological evolution, the latter is always older, stronger, and less willing to compromise.
The Complete Chain and Fundamental Paradox of AI Disenchantment
Through nine layers of progressive analysis and one meta-thesis, this paper reveals the complete causal chain of AI disenchantment:
The unified explanation for all nine layers: AI’s functional evolutionary trajectory is on a head-on collision course with humanity’s biological evolutionary heritage. AI eliminates variables; humans depend on variables. This is a structural contradiction that cannot be engineered away.
The more technology advances, the more perfect the product, the more users reject it. The more invested, the further from the human acceptance threshold.
AI’s value ceiling in utilitarian scenarios is determined by perceptual diminishing returns; its value ceiling in social scenarios is determined by evolutionary psychology. Both ceilings have already been hit. Continued investment will only increase costs, not break through the ceilings.
AI won’t disappear. But its narrative premium — the portion of valuation exceeding true discounted cash flow — will undergo a prolonged compression. AI is shifting from “paradigm” to “function,” and functions are priced by cost, not by dreams.
Ultimately, the deepest layer of AI disenchantment is not humans rationally judging AI as “not good enough,” but the human biological system instinctively saying — “This is not one of my kind.” A brain shaped by 1.5 million years of social primate evolution will not adapt in three years to “pretending” to socialize with machines. This constraint is written in genes, not in code.
The human biological system comes with a built-in fault-tolerance mechanism for other humans’ social behavior — tolerating other humans’ imperfections is the prerequisite for the reciprocal complementarity mechanism. AI’s “perfection-mode” sociality inevitably fails to activate the counterpart human’s fault-tolerance mechanism, triggering rejection instead. Humans inherently reject perfection and anomalies among their own kind.
All factual claims in this paper are based on publicly available sources from December 2025 to April 2026. Listed below in order of citation within each layer.
Layer 1: Macro Narrative Collapse
Layer 2: Social Media & Community Backlash
Layer 3: Frontline Disenchantment with AI as Tool
Layer 4–5: Triple Squeeze — AI Degraded from “Paradigm” to “Function”
Layer 6: Capital Market Narrative Premium Collapse
Layer 7: Perception Ceiling & Scissors Gap
Layer 8: Evolutionary Psychology’s Hard Constraints
Additional References
Layer 9: Social Transmission of Disenchantment [V2 Addition]