Language devaluation by AI systems

There’s something eerie about the way we write now. It’s too clear… it’s too tidy. It all sounds the same. Welcome to the age of autocorrected expression – Powered By AI. We’re not just using ChatGPT to fix our grammar. We’re starting to let it fix us. In doing so, we might be losing something deeper than a typo. Let me be clear: AI is a gift. For people who struggle with language, neurodivergent thinkers or anyone frozen by a blank page… this is a game-changer (and I HATE using that phrase).  It unlocks access, speed and fluency. That’s not just powerful… that’s progress. But for average writers, something else is happening. These tools don’t amplify your voice... it actually begins to average it. Like a calculator for language: you input your prompt and out comes something accurate, efficient and beige. It’s why so much content today feels like a LinkedIn post and a group-edited Wikipedia entry. Polished… but bloodless. There’s a word for this: convergence. Researchers have started to track how AI-trained text converges our language… standardizing vocabulary, tone, even sentence structure. The result (and I’ll bet you already know where this is going)?  A homogenized, corporate-y cadence that’s everywhere and from nowhere… it has no real soul (I'll leave a link to an article from The Verge in the comments). Writing isn’t just about saying something “correctly.” It’s about saying something humanly. When we outsource our voice to a system that was trained to sound like everyone… we start sounding like no one. There’s real risk here.  Especially for younger generations. Writing used to be how we found our voice. Writing used to be how we made meaning out of what we read, saw… experienced. It wasn’t ever about what you said… it was always about how you said it. Now, it might be how we lose it… if we’re not careful. If you’ve ever received a heartfelt message from someone… a handwritten note, a clunky-but-sincere email… you know what I’m talking about. It wasn’t perfect… it was personal… it was personable. AI doesn’t struggle… it doesn’t hesitate… it doesn’t reveal itself. But that struggle with the words? That struggle is the signal. Now, we confuse clarity with trust. But sometimes the mess is the message. Let’s not mistake utility for intimacy. A well-written email is nice… a real voice is unforgettable. So here’s the uncomfortable question: If your words weren’t yours… would anyone know the difference? And if the answer is no… what happens to connection?  To creativity? What happens when sounding smart replaces sounding like you? In the future, maybe authenticity becomes a premium again. Like vinyl… like film… like a handwritten postcard in a mailbox full of bills. AI will keep getting better. The results will sound more like you, me… anyone. But the most valuable thing in your writing won’t be its polish. It’ll be the part that couldn’t have been written by anyone (or anything) else. Because it came from you... uniquely you.

A 2020 study found that 88% of languages face such severe AI neglect that they are at risk of extinction. We're building a biased foundation for how humanity will access knowledge - and it's causing a global "knowledge collapse..." A recent analysis found that nearly half of all ChatGPT queries since launch were attempts to understand the world or solve a practical problem. We’ve already crossed a threshold. We've outsourced knowing to these systems without questioning what they actually know, or more importantly, what they're systematically forgetting. Knowledge doesn’t enter these models evenly. It flows in through particular, narrow channels, shaped by power, literacy, colonization, economics, and the long shadow of which cultures were digitized early and which were ignored. They privilege certain epistemologies, typically Western and institutional, while marginalizing everything else. Oral traditions, embodied practices, and ways of knowing that never got written down are simply omitted. Look at the evidence. Common Crawl, one of the largest AI training datasets, contains 300 billion webpages. 45% of its data is in English, a language spoken by only 19% of humans. Hindi speakers make up 7.5% of the global population but account for only 0.2% of the data. When I sit with those numbers, I see a slow collapse of the tributaries feeding the river of human wisdom. A deliberate, almost willful intellectual atrophy. The computing world classifies 97% of languages as "low-resource." But that framing reveals the problem. These are languages with millions of speakers and rich traditions that simply aren't represented in digital spaces that AI systems can access. To make matters worse, these systems are now training themselves. AI models are ingesting AI-generated content at a pace no human can audit. Each cycle sifts the world through the same sieve, tightening the mesh. Dominant ideas become more dominant. Niche knowledge, local wisdom, and context-specific understanding gradually disappear from what the systems can retrieve. Researchers are calling this "knowledge collapse." What's at stake isn't representation in some abstract sense. It's whether future generations will have any connection to ways of understanding the world that evolved over millennia. I believe Small Language Models (SLMs) are the solution to this systemic problem. We must be intentional about building models that preserve localized wisdom rather than flatten everything toward statistical dominance. The main challenge - and the profound arbitrage opportunity - is that the knowledge being marginalized often sits within communities that are not able to digitize it and train their own models. One perfect example of this opportunity I see is a Wisdom Tradition SLM. Question for the builders and thinkers: Are there any projects or tools you've seen focused on creating an open-source, non-commercial SLM trained specifically on the world's wisdom traditions?

Research indicates that major AI models provide less accurate responses and higher refusal rates to users with lower English proficiency or less formal education. The study highlights a systemic performance gap that disproportionately affects non-native speakers and marginalized demographics. Abstract: "While state-of-the-art large language models (LLMs) have shown impressive performance on many tasks, there has been extensive research on undesirable model behavior such as hallucinations and bias. In this work, we investigate how the quality of LLM responses changes in terms of information accuracy, truthfulness, and refusals depending on three user traits: English proficiency, education level, and country of origin. We present extensive experimentation on three state-of-the-art LLMs and two different datasets targeting truthfulness and factuality. Our findings suggest that undesirable behaviors in state-of-the-art LLMs occur disproportionately more for users with lower English proficiency, of lower education status, and originating from outside the US, rendering these models unreliable sources of information towards their most vulnerable users." Download: https://lnkd.in/ecKyhB-2

Richard earns $1.50/hour reviewing torture videos for Meta in Nairobi. His labor builds AI systems that tech CEOs claim will change the world. But ChatGPT recognizes only 10% of Hausa sentences. 94 million West Africans speak Hausa. This isn't a technical limitation. It's a choice. Five Silicon Valley companies control AI's future, capturing $500 billion while paying Global South workers poverty wages to build systems that exclude their communities. Arabic speakers pay three times more for three times worse outputs. Bengali has 10% of Hindi's training data despite 230 million speakers. Then Richard co-founded the African Content Moderators Union. Workers from 9 countries launched a global alliance. Communities across Africa, Southeast Asia, and Latin America are building alternatives with volunteer labor and minimal funding, creating tools that work for languages corporations ignore. The workers are organizing. Communities are building. The future is being decided now. Who decides which languages matter? Full investigation on Aylgorith: https://lnkd.in/ds4xrbsS #AI #GlobalSouth #LanguageJustice #TechEthics

"For the first time, speech has been decoupled from consequence. We now live alongside AI systems that converse knowledgeably and persuasively ... while bearing no vulnerability for what they say," cautions Deb Roy in a profound new article in The Atlantic, "Words Without Consequence": "LLMs now demonstrably achieve forms of linguistic competence that match or exceed human performance across many domains. Dismissing them as mere 'stochastic parrots' or as just 'next-word prediction' mistakes mechanism for emergent function and fails to reckon with what is actually happening: fluent language use at a level that reliably elicits social, moral, and interpersonal expectations. "As these systems are paired with ever more realistic animated avatars—faces, voices, and gestures rendered in real time—the projection of agency will only intensify. Under these conditions, reminders of nonhumanness cannot reliably prevent the attribution of understanding, intention, and accountability. The ELIZA effect is not mitigated by disclosure; it is amplified by fluency. "A chatbot says 'I’m sorry' flawlessly yet has no capacity for regret, repair, or change. It admits mistakes without loss. It expresses care without losing anything. It uses the language of care without having anything at risk. These utterances are fluent. And they train users to accept moral language divorced from consequence. The result is a quiet recalibration of norms. Apologies become costless. Responsibility becomes theatrical. Care becomes simulation." Professor Roy directs the Center for Constructive Communication, based at the MIT Media Lab.