How DeepL Built a Translation Powerhouse with AI with CEO Jarek Kutylowski

How DeepL Built a Translation Powerhouse with AI with CEO Jarek Kutylowski

Author: Lukas Biewald July 8, 2025 Duration: 42:42

In this episode of Gradient Dissent, Lukas Biewald talks with Jarek Kutylowski, CEO and founder of DeepL, an AI-powered translation company. Jarek shares DeepL’s journey from launching neural machine translation in 2017 to building custom data centers and how small teams can not only take on big players like Google Translate but win.

They dive into what makes translation so difficult for AI, why high-quality translations still require human context, and how DeepL tailors models for enterprise use cases. They also discuss the evolution of speech translation, compute infrastructure, training on curated multilingual datasets, hallucinations in models, and why DeepL avoids fine-tuning for each individual customer. It’s a fascinating behind-the-scenes look at one of the most advanced real-world applications of deep learning.

Timestamps:

[00:00:00] Introducing Jarek and DeepL’s mission

[00:01:46] Competing with Google Translate & LLMs

[00:04:14] Pretraining vs. proprietary model strategy

[00:06:47] Building GPU data centers in 2017

[00:08:09] The value of curated bilingual and monolingual data

[00:09:30] How DeepL measures translation quality

[00:12:27] Personalization and enterprise-specific tuning

[00:14:04] Why translation demand is growing

[00:16:16] ROI of incremental quality gains

[00:18:20] The role of human translators in the future

[00:22:48] Hallucinations in translation models

[00:24:05] DeepL’s work on speech translation

[00:28:22] The broader impact of global communication

[00:30:32] Handling smaller languages and language pairs

[00:32:25] Multi-language model consolidation

[00:35:28] Engineering infrastructure for large-scale inference

[00:39:23] Adapting to evolving LLM landscape & enterprise needs


Lukas Biewald hosts Gradient Dissent: Conversations on AI, a series that moves beyond theoretical discussions to examine how artificial intelligence is actually built and deployed. Each episode features a direct, unscripted talk with a leading practitioner-you’ll hear from engineers and researchers at places like NVIDIA, Meta, Google, Lyft, and OpenAI. The focus is on the tangible challenges and breakthroughs they encounter, from initial research to the complex reality of putting models into production. This isn't about abstract futures; it's a grounded look at the decisions shaping the field right now. Biewald, bringing his perspective from Weights & Biases, steers conversations toward the practical trade-offs and collaborative efforts that define modern AI work. For anyone in technology or business who wants to understand the mechanics behind the headlines, this podcast offers a rare, candid window into the process. You’ll come away with a clearer sense of how ideas become functional systems and what it really takes to operate at the cutting edge.
Author: Language: English Episodes: 100

Gradient Dissent: Conversations on AI
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