Never has a technology sparked as much enthusiasm and distrust as generative artificial intelligence. Capable of producing text, images, code, or even marketing campaigns in seconds, it is redefining standards of productivity and creativity in business. But as its use intensifies, a crucial question arises: what happens when AI feeds on itself, or when it subtly begins to reshape a brand’s messaging?
Research conducted by Stanford, Oxford, Cambridge, and Edinburgh warns of a troubling phenomenon—model collapse—which threatens the integrity of models by gradually stripping them of diversity and factual accuracy. At the same time, Semrush’s analyses for MarTech.com highlight a subtler drift: the progressive alteration of brand voice, contaminated by outdated content or outputs generated by already biased AIs.
Both dynamics converge toward the same danger: the distortion of reality. Far from being a simple technical debate, this is a strategic issue that touches the credibility of data, the authenticity of brands, and ultimately, public trust.
1. AI, a revolution under close surveillance
Generative AI is deeply transforming our relationship with information, communication, and creativity. Businesses now use it to produce marketing content, write scripts, generate images, design code, or assist customers. This technological revolution, driven by large language models (LLMs), seems to open an almost infinite range of possibilities.
But behind the excitement, a silent danger emerges: generative AI can alter reality—not in spectacular or brutal ways, but subtly and cumulatively. Two phenomena converge here:
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Model collapse, identified by researchers at Stanford, Oxford, Cambridge, and Edinburgh, which threatens statistical integrity when models are fed with their own outputs.
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Brand message drift, analyzed by Semrush Enterprise, showing how AI can imperceptibly modify a company’s tone and voice.
These two dynamics reveal a shared truth: without high-level verification processes, generative AI risks polluting data, diluting brand voice, and ultimately reshaping our collective perception of reality.
2. Model Collapse: AI Poisoning Itself
2.1 Definition and mechanics of collapse
Model collapse refers to the phenomenon where an AI model, repeatedly trained on synthetic data (outputs from other AIs), progressively loses diversity and fidelity in the information it contains.
A 2024 study published in Nature (AI models collapse when trained on recursively generated data, Shumailov et al.) shows that this process leads to two forms of collapse:
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Early collapse: the “tails” of the distribution—rare but crucial information that enriches understanding—gradually disappear.
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Late collapse: the model becomes stereotypical and repetitive, generating conventional, impoverished responses.
In short, when constantly fed with its own outputs, AI becomes incapable of innovation or of faithfully reflecting real-world complexity.
2.2 The curse of recursion
In a 2023 arXiv paper (The Curse of Recursion), Shumailov and co-authors extended this observation to various AI architectures (VAEs, GMMs, LLMs). They showed that recursive training on generated data leads to irreversible information loss.
The metaphor is telling: imagine a photocopy of a photocopy, repeated hundreds of times. With each iteration, details disappear, noise accumulates, and the document eventually becomes unreadable.
Applied to AI, this mechanism means statistical reality—already imperfectly captured—inevitably becomes distorted and impoverished.
2.3 Preventing collapse: the role of hybrid data
Fortunately, not all is lost. A team led by Gerstgrasser (arXiv, 2024) suggests a solution: instead of fully replacing human data with synthetic data, they must be combined.
Their study (Is Model Collapse Inevitable?) shows that if generated data supplements, rather than replaces, real data, the model retains performance and avoids collapse. This human + synthetic blend helps stabilize learning.
This aligns with recommendations from Seddik et al. (2024): there is a tolerance threshold for synthetic data usage. Below it, the model remains robust. Beyond it, collapse becomes inevitable.
2.4 Real-world consequences
The implications are significant. Training a language model on AI-generated text can quickly drive it to produce nonsense.
The press has also addressed this issue, warning of “systemic pollution” of digital information (Financial Times, Business Insider).
In other words, if we let AIs self-feed unchecked, we risk creating a loop where data impoverishes, truth erodes, and reality distorts.
3. When AI Alters Brand Voice
3.1 Semrush’s findings: an insidious drift
A study published by Semrush Enterprise on MarTech.com in August (How generative AI is quietly distorting your brand message) describes another danger, complementary to collapse: the progressive distortion of brand voice.
When companies entrust AI with drafting their communications, the system—even if well-calibrated—introduces subtle variations: a slightly different tone, shifted implicit values, standardized phrasing that dilutes brand personality.
In the short term, these shifts are imperceptible. But accumulated over time, they can weaken identity, creating a shadow brand—a ghost brand that exists in the digital space but no longer aligns with the company’s reality.
3.2 Shadow brand data: an invisible enemy
The problem is worsened by what Semrush calls shadow brand data: the set of internal digital assets (old wikis, presentations, internal documents, emails, outdated guides) that feed AI systems.
These contents, often unverified or outdated, contaminate generated outputs. The result: a brand voice no longer consistent with the current strategy, but influenced by residual traces of the company’s past.
4. Convergence: The Reality-Brand Distortion Loop
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Objective: compromised truth → Model collapse undermines factual fidelity, with AI reflecting reality less and less, producing generalities and losing nuance.
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Subjective: blurred identity → Message drift weakens brand voice, introducing subtle shifts that dilute personality and trust.
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The negative reinforcement loop → A weakened AI (collapse) produces biased outputs; these biased outputs feed future datasets (shadow brand). The cycle repeats, creating an alternate reality where neither facts nor brands reflect what they truly are.
We thus enter a vicious cycle of cognitive pollution.
5. The Imperative of Verification and Hybridization
Faced with this dual threat, companies must establish robust verification processes.
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5.1 Ensure source quality → prioritize high-quality human data; clearly label AI-generated content; eliminate outdated shadow brand data.
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5.2 Detection and traceability → deploy watermarking and classification tools to detect AI-generated content; monitor the proportion of synthetic data in training corpora.
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5.3 Editorial governance → set up an AI committee uniting data, communications, and legal experts; define tone guidelines and brand benchmarks as safeguards; conduct regular audits of generated content.
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5.4 Hybrid data strategy → combine human + synthetic data to harness AI benefits without succumbing to collapse; monitor the critical threshold identified in academic research.
6. Operational Implementation
For marketing and communications leaders, this implies concrete actions:
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Map internal sources (wikis, archives, presentations).
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Systematically label AI vs. human content.
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Build a master dataset validated by human teams.
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Regularly audit generated content (tone alignment, legal compliance, narrative coherence).
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Set drift KPIs (tonal deviation, emotional consistency, values fidelity).
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Train teams to detect and correct drifts.
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Establish cross-functional AI governance (data, marketing, legal, ethics).
These practices are not optional—they become a strategic foundation for preserving a company’s credibility and value.
Conclusion: Preserving Reality and Brand in the Age of AI
Generative AI confronts us with a paradox. On one hand, it promises efficiency, creativity, and large-scale personalization. On the other, it risks diluting reality (through model collapse) and drifting brand voice (through shadow brand data).
These phenomena are not spectacular but insidious. They don’t destroy instantly: they gradually erode data quality, reality’s fidelity, and consumer trust.
The solution is not to renounce AI, but to govern it rigorously. That means:
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rigorous data selection,
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high-level verification processes,
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continuous monitoring of brand voice,
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and intelligent human-machine hybridization.
Ultimately, AI’s value will always depend on our ability to preserve truth. And in a world where information is the foundation of trust, truth is the most precious capital.
The future of AI will not depend solely on technological power, but on companies’ ability to use it without being trapped by its own illusions. Progress does not exclude vigilance—it depends on it.
The Saas Advisor Team
