As artificial intelligence reshapes how we work, communicate, and build products, a quiet but fundamental question is emerging: do AI systems actually understand language, or do they merely process it? The difference matters more than most developers admit. Grammar is not decoration. It is the architecture of thought. And building AI tools without understanding that architecture is like designing a city without understanding how people actually move through it.
Iryna Arlova has spent over a decade studying that architecture. An expert in applied linguistics and strategic communication, she earned her degrees at Moscow State University and the Higher School of Economics, the best universities in Russia, and built a practice at the intersection of formal linguistics, cognitive science, and professional learning. She founded GLAGOL by Iryna Arlova, a professional training platform that has served executives at IKEA, the Embassy of Belgium, GeoConsult Germany, and Moscow State University. More recently, she contributed to the development of an AI-driven language platform, Linguix, helping the team secure a $1 million pre-seed investment by translating structural grammar into machine-executable algorithms across three languages.
Now based in Austin, Texas, she is developing Logical Language, a SaaS platform that helps professionals’ structure and communicate complex ideas, and consulting for U.S. companies on cross-cultural communication and international expansion. TechBullion spoke with Iryna about what AI systems still get wrong about language, why linguistics is a technical discipline, and what she calls “the algorithmic view of grammar.”
TechBullion: Iryna, your background is in applied linguistics and cognitive science, not computer science. How did you end up working on AI systems?
Iryna Arlova: The short answer is that the engineers found me, not the other way around. When Linguix, a multilingual AI writing assistant based in Miami, was building their Large Linguistic Model, they reached out after reading an article I had published in a specialized IT blog. What they needed was not another machine learning engineer. They needed someone who understood the deep grammar of multiple languages simultaneously, the kind of understanding that allows you to see not just how a language works, but why it works that way, and how that logic can be encoded into a system.
The longer answer is that I had always believed linguistics was, at its core, a technical discipline. Grammar is a finite system. It has rules, exceptions that follow meta-rules, and patterns of opposition and compensation. I had spent years representing these structures as algorithms for human learners. When AI appeared, I realized the same representation could be used to train machines. The transition felt natural to me, even if it surprised people who expected linguists to stay on the humanities side of the room.
TechBullion: You use the term “algorithmic linguistics.” Is this an established field, or something you developed?
Iryna Arlova: It is a term I use to describe my own approach, though it draws on several established traditions, including formal linguistics, structural grammar in the tradition of Saussure and Chomsky, and cognitive science. The core idea is that a language is a closed, finite system: a limited set of elements, and a set of operations between them. Grammar is not a collection of arbitrary rules. It is a logic, one that is predictable, hierarchical, and learnable if presented correctly.
Most language education treats grammar as a list of facts to memorize. My approach treats it the way you would treat a programming language: here are the elements, here are the operations possible between them, and here is how you generate combinations. A learner who understands the system does not need to memorize, because they can derive. The same is true for a machine. If you give an AI model a structural map of a language rather than a statistical approximation of it, the outputs become more reliable and more explainable.
Among scholars, Noam Chomsky came closest to this view in his early work on generative grammar. But applying it to practical instruction for adult learners, and then to AI training, was largely uncharted territory when I began.
TechBullion: What did your work at Linguix actually involve? How does a linguist contribute to building an AI platform?
Iryna Arlova: The challenge Linguix faced was scalability across languages. They initially focused on English and wanted to expand to French and German. The straightforward approach would have been to build separate models for each language, with separate teams, separate training data, and separate correction logic. That is expensive and creates maintenance problems as the product evolves.
My contribution was developing a unified grammatical framework, a shared structural foundation that describes what all three languages have in common and, critically, where they diverge and how each language compensates for the other’s limitations. For example, English expresses tense through word order and auxiliary verbs, French through verb conjugation, and German through both conjugation and case system. Understanding these as structural equivalents, in other words as different mechanisms achieving the same communicative goal, allowed engineers to build one underlying logic rather than three separate systems.
I also produced detailed specifications for benchmark analysis: how to evaluate whether the model’s corrections were linguistically accurate, not just statistically plausible. There is a significant difference between the two. A statistically common output is not always a grammatically sound one. Linguix has grown to over 300,000 users and has been featured in Fortune, The Washington Post, and Bloomberg. I am proud to have been part of the foundation.
TechBullion: Industry research consistently shows that AI language tools struggle with nuance, cross-cultural context, and professional register. Do you see these as technical problems or linguistic ones?
Iryna Arlova: Both, but the linguistic layer is usually underestimated. The majority of effort in AI language development goes into training data volume and model architecture. Far less attention goes to the structural logic of what the model is actually learning. When a model produces fluent but contextually wrong output, it is usually not a data problem. It is a representational problem: the model has learned to approximate language statistically, but it has not been given a model of what language actually is.
This matters enormously in professional and cross-cultural contexts. Business communication carries layers of meaning beyond the literal: negotiation register, hierarchy signals, face-saving conventions, and the way different cultures structure arguments. A model trained purely on text corpora picks up surface patterns. It does not pick up the underlying logic of why a German executive structures a proposal the way they do, or why an Arabic speaker frames disagreement indirectly where an English speaker would be direct.
My work with corporate clients, including executives relocating to new countries and teams managing international mergers, has shown me that the cost of these misunderstandings is very high. And AI tools, as they are currently built, tend to amplify these misunderstandings rather than resolve them.
TechBullion: Tell us about Logical Language, the platform you are building in Austin. What problem does it solve?
Iryna Arlova: The core problem is one I have observed across years of working with highly qualified professionals: they know exactly what they mean, but cannot reliably translate that knowledge into language that works at a professional level. This is not a vocabulary problem. It is a structural one. When a professional prepares a funding pitch, a project defense, or an executive presentation, the challenge is not finding the right words. The challenge is organizing the thought so that its logic becomes visible to the audience.
What I call “unwinding thoughts and rewinding them into a logical thread” is a process I have been practicing and teaching for years. Logical Language automates part of that process. Unlike general AI tools such as ChatGPT, which produce fluent but often generic and diluted output, this platform is pre-trained on my methodology, the same structural, algorithmic approach I use with human clients. It does not generate content from scratch. It takes the user’s own professional knowledge and helps them articulate it with precision.
We are currently in development with a Silicon Valley-based product manager, Mark Rose, formerly a Senior Product Manager at Meta and Google. We have applied to Y Combinator and are working with the Dell Foundation on implementation and testing. The roadmap moves from MVP to corporate pilots to a nationwide SaaS within twelve months.
TechBullion: You also work as an executive coach and communication strategist, most recently with Creatio in Boston. How does that connect to your technical work?
Iryna Arlova: They are two sides of the same methodology. The executive coaching work is where I see the methodology applied in real time, under real conditions, and with real stakes. When I work with Creatio’s sales director team on international expansion, supporting communication with partners in Australia and structuring narratives for different cultural contexts, I am essentially doing applied linguistics in a business setting. The feedback from that work sharpens the methodology, and the methodology then feeds back into the products I build.
What I have found consistently is that the highest-performing executives are not necessarily the most eloquent speakers. They are the ones who can make their thinking visible, who can take a complex business logic and present it in a sequence that another person can follow and act on. That is a learnable skill. It is also an encodable one. That insight is at the heart of Logical Language.
TechBullion: There is a growing conversation about what AI cannot replace in professional communication. Where do you stand?
Iryna Arlova: I was asked this question in 2021, when generative AI first became widely accessible. My answer then is the same as my answer now: good linguists will not disappear because of AI. They will be the ones who build it.
What AI cannot do, and in my view will not do in any foreseeable timeframe, is create genuine human-to-human communication: the kind that builds trust, conveys values, and navigates the complexity of relationship. AI can assist. It can structure, suggest, and scale. But the communicative act itself, namely the decision about what to say, when, and to whom, remains deeply human. And for professionals operating in international, cross-cultural environments, that act is extraordinarily consequential.
My goal is not to replace that act. It is to support it with better tools, better structure, and a better understanding of what language actually is.
TechBullion: What is your broader vision for where this work leads, both for you and for the field?
Iryna Arlova: I believe we are at the beginning of a serious reckoning with what language competence means in an AI-augmented world. For the last decade, the dominant assumption has been that AI will handle language and humans will handle everything else. That assumption is being revised. Language is not a neutral medium. It is the medium through which decisions are made, alliances are built, and meaning is created. An AI that does not understand language deeply does not understand the world deeply.
My work, including the methodology, the school, the AI collaboration, and the products I am building, is an attempt to construct a more rigorous bridge between formal linguistics and practical application. I want to demonstrate that the humanities, done rigorously, are technical disciplines. That grammar is mathematics. That communication is an engineering problem with humanistic stakes.
For the United States specifically, I see enormous opportunity. The U.S. is at the center of AI development, but the linguistic expertise required to build AI that truly understands multilingual, cross-cultural communication is scarce. That is a gap I intend to help close, through Logical Language, through consulting, and through the methodology that has taken me fifteen years to develop and validate.
Iryna Arlova is an applied linguist and communication strategist based in Austin, Texas. She is the founder of GLAGOL by Iryna Arlova and the creator of Logical Language, a SaaS platform for professional communication. She has consulted for companies across Russia, Europe, and the United States, and contributed to AI development at Linguix. She holds degrees from Moscow State University and the Higher School of Economics.
