In this episode of Gradient Dissent, Lukas Biewald sits down with Arvind Jain, CEO and founder of Glean. They discuss Glean's evolution from solving enterprise search to building agentic AI tools that understand internal knowledge and workflows. Arvind shares how his early use of transformer models in 2019 laid the foundation for Glean’s success, well before the term "generative AI" was mainstream.

They explore the technical and organizational challenges behind enterprise LLMs—including security, hallucination suppression—and when it makes sense to fine-tune models. Arvind also reflects on his previous startup Rubrik and explains how Glean’s AI platform aims to reshape how teams operate, from personalized agents to ever-fresh internal documentation.

Follow Arvind Jain: https://x.com/jainarvind

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Timestamps:

[00:01:00] What Glean is and how it works

[00:02:39] Starting Glean before the LLM boom

[00:04:10] Using transformers early in enterprise search

[00:06:48] Semantic search vs. generative answers

[00:08:13] When to fine-tune vs. use out-of-box models

[00:12:38] The value of small, purpose-trained models

[00:13:04] Enterprise security and embedding risks

[00:16:31] Lessons from Rubrik and starting Glean

[00:19:31] The contrarian bet on enterprise search

[00:22:57] Culture and lessons learned from Google

[00:25:13] Everyone will have their own AI-powered "team"

[00:28:43] Using AI to keep documentation evergreen

[00:31:22] AI-generated churn and risk analysis

[00:33:55] Measuring model improvement with golden sets

[00:36:05] Suppressing hallucinations with citations

[00:39:22] Agents that can ping humans for help

[00:40:41] AI as a force multiplier, not a replacement

[00:42:26] The enduring value of hard work

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

In this episode of Gradient Dissent, Lukas Biewald sits down with Thomas Dohmke, CEO of GitHub, to talk about the future of software engineering in the age of AI. They discuss how GitHub Copilot was built, why agents are reshaping developer workflows, and what it takes to make tools that are not only powerful but also fun.

Thomas shares his experience leading GitHub through its $7.5B acquisition by Microsoft, the unexpected ways it accelerated innovation, and why developer happiness is crucial to productivity. They explore what still makes human engineers irreplaceable and how the next generation of developers might grow up coding alongside AI.

Follow Thomas Dohmke: https://www.linkedin.com/in/ashtom/

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