{"id":2076415,"date":"2026-08-28T15:37:10","date_gmt":"2026-08-28T13:37:10","guid":{"rendered":"https:\/\/www.luxsure.fr\/2026\/08\/28\/when-language-ceases-to-be-a-barrier-deepls-quiet-promise\/"},"modified":"2026-08-28T15:44:52","modified_gmt":"2026-08-28T13:44:52","slug":"when-language-ceases-to-be-a-barrier-deepls-quiet-promise","status":"publish","type":"post","link":"https:\/\/www.luxsure.fr\/en\/2026\/08\/28\/when-language-ceases-to-be-a-barrier-deepls-quiet-promise\/","title":{"rendered":"When Language Ceases to Be a Barrier: DeepL’s Quiet Promise"},"content":{"rendered":"\n

VivaTech 2026 \u2013 Interview with Jarek Kutylowski, CEO and Co-Founder of DeepL<\/a><\/strong><\/p>\n\n

For a long time, machine translation was simply a matter of convenience. An email you needed to understand quickly. A foreign webpage you needed to decipher. A document you wanted to skim through without hiring a professional translator.<\/p>\n\n

That era is coming to an end.<\/p>\n\n

At VivaTech 2026, Jarek Kutylowski, founder of DeepL, championed a far more ambitious vision: that of a world where language becomes an invisible layer of the human experience. No longer an obstacle to be circumvented, but a technological constraint that artificial intelligence is gradually erasing.<\/p>\n\n

The challenge is no longer translation. It is communication.<\/p>\n\n

From a productivity tool to a communication infrastructure<\/h2>\n\n

Four years ago, DeepL was primarily known as a particularly high-performing translation tool. Its success was based on a simple, viral product that was far superior to the available alternatives.<\/p>\n\n

Today, the situation is different.<\/p>\n\n

The explosion of generative AI has turned translation into a standard feature. Every major model is capable of translating text. The question, therefore, is no longer whether a machine can translate, but to what degree of accuracy, reliability, and domain-specific adaptation it can do so.<\/p>\n\n

For Kutylowski, the answer lies in a clear commitment to specialization.<\/p>\n\n

The German company isn\u2019t trying to become a one-size-fits-all assistant. It aims to solve organizations\u2019 most critical communication challenges: complex legal contracts, technical documentation, sensitive multilingual exchanges, and international collaboration.<\/p>\n\n

This strategy illustrates a broader trend in the AI market: value is gradually shifting from generic capabilities toward vertical expertise.<\/p>\n\n

The End of the \u201cFree\u201d AI Myth<\/h2>\n\n

One of the most revealing parts of the interview concerns the actual cost of the models.<\/p>\n\n

In 2017, the executive explains, developing a high-performance translation model required a few hundred thousand euros\u2019 worth of computing power.<\/p>\n\n

Today, the costs run into the tens or even hundreds of millions.<\/p>\n\n

To remain competitive, DeepL must now train very large models comparable to the architectures used in the world of LLMs.<\/p>\n\n

This cost inflation is profoundly changing the nature of the industry.<\/p>\n\n

For several years, AI innovation was driven by a relatively accessible approach to experimentation. Now, every investment in research must be justified by a business model capable of absorbing the cost.<\/p>\n\n

In other words: the question is no longer just about building a better model. It\u2019s about who will still be able to finance this race five years from now.<\/p>\n\n

Why DeepL Refuses to Depend on American Giants<\/h2>\n\n

While many companies build their products using APIs from major American players, DeepL continues to develop its own models.<\/p>\n\n

This is a strategic choice.<\/p>\n\n

According to Kutylowski, owning the entire technology stack offers two major advantages:<\/p>\n\n

    \n
  • maintain superior quality for specific applications;<\/li>\n\n\n\n
  • incorporate customer feedback into the models much more quickly.<\/li>\n<\/ul>\n\n

    This line of reasoning echoes a distinction that has become central to the AI industry: that between “overlays” and “builders.”<\/p>\n\n

    The former depend on the technological decisions of other players. The latter control their own destiny but bear much higher costs.<\/p>\n\n

    DeepL has clearly chosen its side.<\/p>\n\n

    The Next Battleground: Voice<\/h2>\n\n

    The most significant announcement at the conference, however, had nothing to do with text.<\/p>\n\n

    The day before the presentation, DeepL had officially announced the integration of the team from MixHalo, a California-based company specializing in real-time voice translation.<\/p>\n\n

    The acquisition marks a significant milestone in the company\u2019s evolution.<\/p>\n\n

    Until now, most of its value has come from document translation. Now, DeepL is tackling a much more complex challenge: enabling two people to converse naturally in their native languages while understanding each other instantly.<\/p>\n\n

    The difference is significant.<\/p>\n\n

    A document can be analyzed, proofread, and corrected.<\/p>\n\n

    A conversation requires preserving emotion, intent, cultural nuances, rhythm, and sometimes even humor\u2014all with just a few hundred milliseconds of latency.<\/p>\n\n

    The challenge is as much linguistic as it is human.<\/p>\n\n

    The real product isn\u2019t the translation<\/h2>\n\n

    The most interesting part of the conversation comes when Kutylowski talks about his own family.<\/p>\n\n

    As a Pole living in Germany who speaks several languages on a daily basis, he observes firsthand how each language conveys a particular way of seeing the world.<\/p>\n\n

    The perfect translation has never been about replacing one word with another.<\/p>\n\n

    It involves conveying a cultural context.<\/p>\n\n

    This is precisely why translation remains a difficult technological challenge despite the spectacular advances in generative AI.<\/p>\n\n

    Models must understand not only what is said, but also what is implied.<\/p>\n\n

    For luxury brands, cultural institutions, or heritage organizations, this distinction is essential.<\/p>\n\n

    Translating a text is relatively simple.<\/p>\n\n

    Translating an identity is another matter entirely.<\/p>\n\n

    The Detail<\/h2>\n\n

    Most observers view language models as conversational tools. DeepL uses them differently.<\/p>\n\n

    To achieve a professional level of translation, the company says it must now leverage architectures comparable to those of major LLMs, with hundreds of billions of parameters.<\/p>\n\n

    Machine translation has thus become a very large-scale computational problem, with investments now on par with those of the world\u2019s leading AI companies.<\/p>\n\n

    When the Boundary Disappears<\/h2>\n\n

    For decades, technology has promised a “universal translator” worthy of science fiction.<\/p>\n\n

    For the first time, this goal seems technically achievable.<\/p>\n\n

    The question then becomes less about technology and more about society.<\/p>\n\n

    What happens when language ceases to be a barrier to access to information, education, trade, or international collaboration?<\/p>\n\n

    For Kutylowski, the answer is simple: more exchange, more mutual understanding, and more opportunities.<\/p>\n\n

    But another question deserves to be asked.<\/p>\n\n

    If language barriers disappear, will the cultural diversity they sometimes protected also disappear?<\/p>\n\n

    The future of linguistic AI may not hinge on its ability to make everyone speak the same language.<\/p>\n\n

    It will depend on its ability to allow everyone to continue speaking their own language.<\/p>\n\n

    \n\n

    <\/h3>\n\n
    \"\"<\/figure>\n","protected":false},"excerpt":{"rendered":"

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