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
