The author wrote previously for Stankevicius “The Moloch Trap: OpenAI’s Evolution and the Paradox of Progress,” ( December 2, 2024) and on “AI’s Potemkin Vision: The Seduction of Educational Techno-Solutionism,” (November 18, 2024) where she explored how the rapid ascent of artificial intelligence (AI) has captivated Higher Education Institutions (HEIs), heralding promises of unprecedented technological advancements in education. She pointed out that “beneath this veneer of progress lies a complex web of epistemological and pedagogical quandaries that question the very foundation of AI’s trajectory in education. While large language models (LLMs) demonstrate a remarkable ability to detect patterns, the author posits that they fundamentally misconstrue the nature of correlative thinking, which emerges from lived experience, cultural wisdom, and the body’s deep knowing.” (Cowin, AI’s Potemkin Vision: The Seduction of Educational Techno-Solutionism) Enter Duolingo’s AI rollout.
The Antaeus Paradox in Duolingo
We all know the allure of invincibility. In Greek mythology, the giant Antaeus, son of Poseidon (the sea) and Gaea (the Earth), possessed such power. He was unbeatable, a fearsome wrestler who drew limitless strength directly from his mother, the Earth. As long as Antaeus remained in contact with the ground, his victory was assured. But Heracles, the hero of many labors, discovered his secret. By lifting Antaeus into the air, severing his connection to Gaea, Heracles thus disconnected him from his power source. 
The “Antaeus problem” in this article metaphorically refers to a situation where an entity (be it a person, organization, idea, or strategy) relies on a fundamental connection to a specific source for its strength, viability, or power. The “problem” arises when this connection is weakened, threatened, or severed, leading to the entity’s diminished capacity or failure. Enter Duolingo’s CEO, Luis von Ahn, who recently announced a company-wide strategic pivot to an “AI-first” company. Von Ahn has a computer science degree and a long interest in games and language learning, foreshadowed by his 2013 paper “Duolingo: learn a language for free while helping to translate the web,” published in the IUI’13: Proceedings of the 2013 International Conference on Intelligent User Interfaces in March 2013. He stated then:
I want to translate the Web into every major language: every webpage, every video, and, yes, even Justin Bieber’s tweets. With its content split up into hundreds of languages — and with over 50% of it in English — most of the Web is inaccessible to most people in the world. This problem is pressing, now more than ever, with millions of people from China, Russia, Latin America and other quickly developing regions entering the Web. In this talk, I introduce my new project, called Duolingo, which aims at breaking this language barrier, and thus making the Web truly “world wide.”
von Ahn, 2013
He attributes the rapid and large-scale nature of this expansion to its use of generative AI. “Developing our first 100 courses took about 12 years, and now, in about a year, we’re able to create and launch nearly 150 new courses.”
Yet, might the specter of Antaeus loom? Could this rapid, AI-driven expansion, an overzealous embrace of AI, or a mismanaged integration, inadvertently lift Duolingo from its own foundational strengths, risking a similar fate to the ill-fated “stubborn combatant?” (Plato, Theaetetus 169b) And crucially, does automating course content for such a vast array of languages, especially those rich in nuance, risk creating a generation of “robotic” language teaching?
Duolingo’s Break with Gaea: The Risks of Scaling Language Without Roots
Just as Antaeus drew lifeblood from Gaea, Duolingo’s phenomenal success is rooted in several core foundations. These are the “grounding principles” that have nourished its growth and user loyalty:
- Curated Pedagogical Approach & Content Quality: Duolingo didn’t just throw words at users. Its courses, historically designed by language learning experts, offered a structured, deliberate pedagogical progression. Much of its content, though gamified and repetitive for reinforcement, was human-crafted or heavily vetted by linguists and translators, ensuring a degree of accuracy, cultural nuance, and pedagogical relevance.
- Human Expertise and Oversight: Behind the playful interface were linguists, curriculum designers, and language teaching specialists. Even the contractors, whose roles are now being re-evaluated, provided an implicit layer of quality control, native speaker intuition, and diverse perspectives that AI, in its current state, might struggle to replicate consistently across 148 new fronts simultaneously.
- User Trust and Engagement: Millions trust Duolingo to provide a reasonably effective and accurate learning experience. This trust is built on the perceived quality of its content and its ability to motivate learners through gamification and a sense of progress.
These elements constitute Duolingo’s “Earth” as the source of its strength and effectiveness.
When Scale Becomes Hercules
Heracles didn’t defeat Antaeus through brute force alone, but through a strategic understanding of his opponent’s strength. When Scale Becomes Hercules captures the moment when rapid expansion, driven by AI, turns from innovation into disruption. In Duolingo’s case, scale may act like Hercules, lifting the platform away from its foundation in human-guided pedagogy, cultural nuance, and learner trust. The risk lies not in growth alone but in growth that is disconnected from the principles that organizationally ground its educational effectiveness. This shift invites a closer examination of how scale, when unchecked or misaligned with core pedagogical values, can undermine rather than enhance educational platforms. When scale becomes Hercules:
- The “Small Hits on Quality” Gamble, Magnified: The explicit acceptance of “occasional small hits on quality” in the pursuit of speed and scale becomes even more critical when launching 148 new courses. The author believes that language learning is contextually and culturally sensitive. What AI deems a “small hit”, like an awkward phrase, a subtle mistranslation, a missed idiom, or a culturally tone-deaf example, can potentially confuse learners, ingrain incorrect patterns, and erode user confidence. Cumulatively, across a vastly expanded portfolio, these small hits could significantly degrade pedagogical value, akin to Antaeus being slowly but surely lifted inch by inch.
- The Risk of “Robotic” Language Teaching: Will course content for highly nuanced languages create “robotic” teaching and become a direct challenge to the quality of the learning experience? While AI can generate grammatically correct sentences and translate content for “shared content” systems, can it truly capture the idiomatic richness, cultural subtleties, humor, and engaging quirks that make language learning (and speaking in contextually rich real-world spaces) come alive? If the new courses, particularly for languages requiring deep cultural understanding (like Japanese, Korean, and Mandarin, which are part of this expansion), feel sterile, overly literal, or lack authentic voice, learners may disengage or, worse, learn an unnatural form of the language. This is the homogenization risk writ large.
- The Diminishing Human Touch at Scale: The plan to “gradually stop using contractors to do work that AI can handle” is tested by such a massive rollout. While the company states AI allows them to “focus our expertise where it’s most impactful,” the sheer volume of new content for beginner levels (CEFR A1-A2) across so many languages raises questions about the depth of human linguistic and pedagogical review possible for each specific language pair. It marginalizes human expertise, replacing nuanced cultural insights with generalized algorithmic content. Language experts provide detailed input, spotting cultural inappropriateness or subtle errors that a generalized AI model, even one customized via “shared content” systems, might miss. Reducing this human oversight is like weakening Antaeus’s connection to Gaea.
- The Specter of Homogenization and Bias (Amplified): Over-reliance on AI for content creation, especially when scaling rapidly, risks generating bland, statistically probable but uninspired content. The “shared content” approach, while efficient, could lead to a core curriculum that, despite customization, loses the unique flavor and specific pedagogical needs of diverse language learners. AI models can also perpetuate biases present in their training data. Ensuring that 148 new courses are free from such biases and culturally sensitive is a monumental task. Who is checking the content?
- Model Drift and Opaque AI: As AI models generate more content and learning paths, the risk of “drift” or the “black box” problem increases. If Duolingo’s AI becomes a “black box,” generating content or learning paths not fully understood by human educators for this vast new range of courses, it becomes difficult to ensure pedagogical soundness as the system becomes ungrounded from human-comprehensible educational principles.
- Algorithmic Bias in Content: AI models are trained on vast datasets, which can contain inherent biases (social, cultural, linguistic). If AI-generated content is not continuously audited, these biases can creep into learning materials, presenting skewed perspectives or reinforcing stereotypes or misinformation.
Empty Glory or True Pedagogical Might? A Platonic Perspective
The philosopher Plato, in his Laws (796a), touched upon a similar theme when discussing skills pursued for reasons other than true utility. He noted: “As to the devices introduced by Antaeus or Cercyon in the art of wrestling for the sake of empty glory… since they are useless in the business of war, they merit no eulogy.”
This raises a critical question for Duolingo, especially in light of its massive new course launch: Is the “AI-first” push, and the resulting ability to deploy nearly 150 courses in under a year, primarily aimed at enhancing the “business of war” – that is, genuinely improving language education and learner outcomes with depth and nuance? Or could it, in part, be a pursuit of “empty glory” – impressive AI metrics, massive content scaling for its own sake, or technological prowess that doesn’t fully translate into better, more authentic learning?
If AI is used to optimize for what it can easily produce and measure (like the number of courses or basic sentence structures), rather than for holistic, culturally rich language proficiency, Duolingo risks developing “devices” that, like Antaeus’ wrestling tricks for show, lack true pedagogical substance. The acceptance of “small hits on quality” in favor of unprecedented speed and scale could be seen as prioritizing a form of scalable “glory” over the hard-won effectiveness that comes from meticulous, human-guided, and culturally attuned course creation.
For this author, the adapted Antaeus metaphor reveals a deeper structural danger. When scale becomes Heracles and lifts a system away from the sources that once gave it vitality, such as cultural grounding, human insight, or lived complexity, initial success may conceal a broader unraveling. What begins as acceleration can result in disconnection, where efficiency overtakes authenticity, and systems built to serve become estranged from those they were meant to empower.
Dr. Jasmin (Bey) Cowin, a columnist for Stankevicius, employs the ethical framework of Nicomachean Ethics to examine how AI and emerging technologies shape human potential. Her analysis explores the risks and opportunities that arise from tech trends, offering personal perspectives on the interplay between innovation and ethical values. Connect with her on LinkedIn.
