Why Language May Be Holding Back the Next Generation of Innovators

Have you ever worn night‑vision goggles in broad daylight?

These goggles are magical tools at night, letting you see what humans were never built to detect. But the moment you step into the sun, they become a handicap. At that point, simple sunglasses — unremarkable pieces of glass with a little tint, are perfectly suited for the job. Nothing magical, nothing advanced — just exactly what you need.

Languages function in a similar way. Indigenous languages are incredibly rich for cultural expression, emotional nuance, and social cohesion — in these contexts, they are like night-vision goggles, providing capabilities unmatched by other tools. But when the goal is systematic innovation, a language optimized for abstract reasoning, technical precision, and cumulative knowledge is required.

Trying to build cutting-edge AI or teach advanced scientific concepts entirely in an indigenous language without prior technical scaffolding is like trying to drive through daylight with night-vision goggles: impressive in theory, but completely impractical.

I’ve had some experience with 3D design software, so thinking of the word “bevel” immediately conjures a geometric image in my mind — an edge being extruded in three-dimensional space. I can mentally add color, change its texture, or even simulate physics, creating hyperrealistic visualisations from just that one word. This cognitive ease stems from English’s rich technical vocabulary, which provides precise terms that facilitate the visualisation of complex concepts.

When a language lacks these terms, speakers may struggle to conceptualise them, leading to a cognitive blind spot.

The Sapir-Whorf Hypothesis

The Sapir-Whorf hypothesis posits that the structure and vocabulary of one’s language influence the way individuals perceive and think about the world. This concept, known as linguistic relativity, suggests that language is not merely a tool for communication but a framework that shapes cognition and worldview.

In the context of innovation, this means that the language in which individuals are immersed from an early age can significantly impact their ability to conceptualize and engage with complex, abstract ideas. A language rich in technical vocabulary and abstract concepts provides a cognitive scaffold that facilitates the understanding and development of innovative ideas.

Cognitive Development and Early Language Acquisition

Cognitive development theories emphasize the importance of early experiences in shaping an individual’s intellectual abilities. The language spoken during formative years plays a crucial role in this process. For instance, languages that have developed extensive technical and scientific vocabularies enable children to grasp complex concepts more readily.

In contrast, languages with limited technical lexicons may require individuals to translate or adapt concepts from other languages, potentially introducing cognitive friction that can impede the learning process. This scenario is particularly evident in societies where education systems predominantly use a language that lacks the necessary technical vocabulary, leading to challenges in accessing and applying advanced knowledge.

Case Studies of Language and Innovation Across Cultures

Language not only influences individual cognition but also shapes the broader culture of innovation within a society. A language that supports the expression of complex and abstract ideas encourages a culture that values and pursues innovation.

In societies where the dominant language lacks the capacity to express such ideas, there may be a cultural tendency to focus on immediate, practical concerns rather than long-term, abstract thinking. This cultural orientation can affect educational priorities, research and development efforts, and the overall capacity for innovation.

Japan and South Korea

Japan and South Korea have demonstrated remarkable innovation in various technological sectors. A significant factor contributing to this success is the development and use of their native languages in scientific and technical domains.

In Japan, the government and academic institutions have invested in creating and standardizing technical terms in the Japanese language. This effort has ensured that students and professionals can engage with complex subjects without the barrier of language translation. Similarly, South Korea has implemented policies to promote the use of Korean in scientific research, fostering an environment conducive to innovation.

Germany

Germany’s approach to innovation is deeply intertwined with its linguistic infrastructure. The German language has a long history of accommodating technical and scientific terminology, which has been integrated into the education system. This linguistic capability allows German-speaking individuals to engage with advanced concepts from an early age, providing a solid foundation for innovation.

Nigeria

Nigeria faces a unique linguistic landscape that shapes how people encounter technical knowledge. Although English is the official language and the medium of instruction, it is not the language most Nigerians actually think or communicate in on a daily basis. For many, early cognitive development happens primarily in an indigenous language, and everyday interaction is dominated by Pidgin — a simplified, highly flexible form of English optimised for social communication, not technical abstraction.

This creates a layered challenge. Students may learn science or mathematics in formal English, but the conceptual foundation built at home and in their communities is structured around Yoruba, Igbo, Hausa, or Pidgin. None of these languages currently provide the dense, standardised technical vocabulary that advanced reasoning requires. Pidgin in particular, while excellent for fluid social exchange, compresses meaning and emphasises immediacy over precision. It strips language down for speed and accessibility, which makes it even harder to use as a cognitive tool for complex or abstract ideas.

As a result, many learners must juggle three linguistic systems:

the indigenous language they grew up with, rich but not structured for technical domains,
Pidgin, which dominates informal communication but lacks the precision needed for scientific reasoning,
and formal English, which carries the technical scaffolding but is often not deeply internalised.

This triple-layered environment increases cognitive friction, where students may recognise technical terms in English but struggle to anchor them in a conceptual framework built through their day-to-day languages. The mental switching required to translate, adapt, or approximate ideas across these systems makes it harder to reason about mechanisms, model abstractions, or build on prior knowledge.

In effect, Nigeria's linguistic reality produces a situation where no single language provides a seamless path from childhood cognition to advanced technical thought. And that mismatch often explains why systematic innovation feels harder than it should be.

The Challenge of Translating Technical Concepts into Local Languages

While Yoruba is rich in cultural and everyday vocabulary, its lexicon for technical and abstract concepts is less developed. For instance, the Yoruba translation for “geometry” is “geometri,” a direct borrowing from English. This borrowing indicates a gap in the indigenous vocabulary for certain technical terms.

Imagine attempting to explain a complex technical document, such as one detailing rocket propulsion, entirely in Yoruba. The original English explanation might read:

“Rocket propulsion operates on Newton’s third law of motion: for every action, there is an equal and opposite reaction. In a rocket engine, fuel and an oxidizer are mixed and ignited in a combustion chamber. The resulting high-pressure and high-temperature gases are expelled through a nozzle, producing thrust that propels the rocket forward.”

Attempting to translate this into Yoruba without borrowing English terms presents a significant challenge. Concepts like “oxidizer,” “combustion chamber,” and “thrust” don’t have direct equivalents in Yoruba, requiring lengthy descriptions or simplifications. This process loses the precision and nuance of the original explanation.

Yoruba Translation (No English Borrowing):

Ẹ̀kọ́ nípa bí ohun tí ń fò ṣe ń ṣiṣẹ́ da lori ofin Newton tí ó sọ pé fún gbogbo ohun tí a ṣe, ohun kan wà tí ó dájú àti ohun kan wà tí ó lòdì sí i. Nínú ohun tí ń fò kan, epo àti ohun tí ń tan ina ni a dá pọ̀ sínú yàrá tí ń jó. Gáàsì tó lágbára gan-an àti tó gbóná gan-an ń bọ láti inú ìṣúra kékeré kan, tí ó sì ń fa ohun tí ń fò lọ síwájú.

“Oxidizer” → “ohun tó ń tan ina” (thing that shows light)
“Combustion chamber” → “ìyẹ̀wù tí ó n jó” (chamber that burns)
“Thrust” → “ń fa ọkọ̀ òfurufú lọ síwájú” (pushes the sky vehicle forward)

Lesson on how a flying thing works is based on Newton’s law that says for every action, there is something sure and something opposite. In a flying thing, fuel and the thing that shows light are mixed together in a burning room. Gas that is very strong and very hot comes out from a small tube, and it pushes the flying thing forward.

Sounds like magic now doesn’t it?

Notice how this captures the observable effects and basic actions without any technical or chemical precision. Someone unfamiliar with English scientific concepts would only get a rough, literal understanding — enough to imagine it happening, but not enough to reason about mechanisms or innovate.

If you have to be bilingual for these things to make sense to you, why not go straight to the source?

Implications for Innovation in Post-Colonial Societies

The challenges faced when translating technical concepts into Yoruba are not unique to this language. Many post-colonial societies grapple with similar issues, where indigenous languages have not evolved to express modern scientific and technical ideas. This linguistic gap can impede the development of a culture of innovation, as individuals may struggle to engage with complex concepts in their native languages.

The ability to conceptualize and engage with complex ideas is facilitated by a language’s capacity to express those ideas. When a language lacks the necessary vocabulary, cognitive friction arises, hindering the process of innovation.

By developing and standardizing technical vocabulary in indigenous languages like Yoruba, societies can empower individuals to engage with advanced knowledge and contribute to a culture of innovation. This endeavor not only preserves linguistic heritage but also fosters progress and development in a rapidly evolving world.

Language, Temporal Orientation, and Development Outcomes

The interplay between language and cultural focus has tangible effects on a society’s capacity to innovate. When a language emphasises immediate or past-oriented phenomena over abstract, future-oriented reasoning, it subtly shapes how people perceive problems and solutions. Individuals may excel at navigating social, relational, or ritualised contexts, but struggle to conceptualise complex systems or plan multi-step innovations.

There’s a lot of history to show this:

Many African societies historically prioritised practical, immediate knowledge for survival, agriculture, and community cohesion. While highly adaptive in those contexts, this focus did not necessitate abstract technical frameworks, which are essential for mechanised, industrial, or aerospace innovation.
By contrast, European languages like English, German, or French developed in contexts emphasising systematic abstraction, scientific documentation, and conceptual modeling. These linguistic tools made it easier to transmit technical knowledge across generations and support cumulative innovation.
In modern post-colonial contexts, highly skilled Africans who succeed abroad — in fields such as aerospace, mathematics, or engineering — almost universally operate in English or other technical languages. Their success depends not on their local languages, but on the cognitive scaffolding and abstraction English provides, allowing them to reason about processes, mechanisms, and future possibilities.

The lesson is not that indigenous languages are inferior — they are culturally rich, expressive, and perfectly suited for many forms of human thought.

Rather, when the goal is systematic innovation, languages that lack technical vocabulary and future-oriented abstraction create a subtle barrier. Even highly intelligent individuals face cognitive friction if they must translate or reconstruct abstract concepts from a lexicon that prioritises immediate, relational, or emotional experience.

One of the reasons Nigerians and other Africans succeed in high-tech or STEM fields abroad is that they operate in English or other globally technical languages. Their success is due to the cognitive scaffolding these languages provide, not the indigenous ones.

Recognising this dynamic helps explain why some societies, despite abundant human potential, may struggle to achieve sustained technological and scientific development. It also underscores the importance of strategically cultivating languages, education, and cognitive tools that enable abstraction, modeling, and experimentation — all critical for innovation-driven progress.

Optimising for Innovation Vs Cultural Preservation

Let me be blunt:

If your goal is innovation, focus on the languages that already enable it. If your goal is culture, identity, or community, pursue your local language. Expecting every language to be equally capable of driving cutting-edge innovation is unrealistic.

Many indigenous languages are not built for abstract, technical, or scientific reasoning. Investing significant time and energy into building AI systems in them is unlikely to benefit most people. At best, you may gain niche recognition or work in highly specialized roles. For the vast majority, it’s a high-effort, low-return pursuit.

Indigenous languages are rich, expressive, and essential for identity. But in a world where innovation drives global opportunity, practical limits exist.

Digitization, archiving, and research are worthwhile, but the idea that all languages can become globally useful on the scale of English, French, or Mandarin is fantasy.

Even with advanced AI translation, the cognitive scaffolding required to truly innovate in a non-technical language would take decades to develop. The surface-level fluency creates the illusion of progress while leaving the underlying gap unaddressed.

In countries struggling with infrastructure, education, energy, and governance, building AI in non-standardized indigenous languages is far from strategic. Multinational corporations are not going to restructure global workflows for languages that provide no economic leverage. These projects often end up symbolic — gestures of inclusion, not engines of innovation.

If someone enjoys the intellectual challenge or seeks cultural preservation, I respect that. But for most people, this is not a practical path. Pouring energy into these languages without addressing systemic and structural limitations is a formula for wasted effort.

Choose Your Tools Wisely

The reality is simple: languages are tools. Some are designed for cultural richness, social nuance, and emotional depth. Others are optimized for abstraction, technical reasoning, and cumulative innovation.

Expecting every language to excel in every domain is like trying to navigate the sunlit world with night‑vision goggles: theoretically possible, but unnecessarily cumbersome and ultimately limiting.

Advanced AI translation might make it seem as though indigenous languages can suddenly support cutting-edge innovation. On the surface, it looks like progress. But without decades of technical scaffolding, teaching, and conceptual standardization, this progress is largely illusory. It deepens cognitive friction rather than alleviating it, creating the sense that we’re “on par” with English or Mandarin while actually widening the gap.

This is not a rejection of cultural or linguistic pride. Indigenous languages are invaluable for identity, social cohesion, and expression. But for individuals and societies aiming to innovate at a global scale, the strategic choice is clear: focus on languages that already provide the cognitive scaffolding for complex reasoning. Use indigenous languages to preserve heritage, share culture, and strengthen community — but don’t expect them to replace the technical tools English and other globally dominant languages provide.

Pick the right tool for the job. Knowing when to wear night-vision goggles and when to reach for simple sunglasses may be the single most important step toward cultivating true, sustainable innovation.