In this episode of Gradient Dissent, Lukas Biewald talks with the CEO & founder of Surge AI, the billion-dollar company quietly powering the next generation of frontier LLMs. They discuss Surge's origin story, why traditional data labeling is broken, and how their research-focused approach is reshaping how models are trained.

You’ll hear why inter-annotator agreement fails in high-complexity tasks like poetry and math, why synthetic data is often overrated, and how Surge builds rich RL environments to stress-test agentic reasoning. They also go deep on what kinds of data will be critical to future progress in AI—from scientific discovery to multimodal reasoning and personalized alignment.

It’s a rare, behind-the-scenes look into the world of high-quality data generation at scale—straight from the team most frontier labs trust to get it right.

Timestamps:

00:00 – Intro: Who is Edwin Chen?

03:40 – The problem with early data labeling systems

06:20 – Search ranking, clickbait, and product principles

10:05 – Why Surge focused on high-skill, high-quality labeling

13:50 – From Craigslist workers to a billion-dollar business

16:40 – Scaling without funding and avoiding Silicon Valley status games

21:15 – Why most human data platforms lack real tech

25:05 – Detecting cheaters, liars, and low-quality labelers

28:30 – Why inter-annotator agreement is a flawed metric

32:15 – What makes a great poem? Not checkboxes

36:40 – Measuring subjective quality rigorously

40:00 – What types of data are becoming more important

44:15 – Scientific collaboration and frontier research data

47:00 – Multimodal data, Argentinian coding, and hyper-specificity

50:10 – What's wrong with LMSYS and benchmark hacking

53:20 – Personalization and taste in model behavior

56:00 – Synthetic data vs. high-quality human data

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https://twitter.com/weights_biases

https://www.linkedin.com/company/wandb

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

Follow Weights & Biases: https://x.com/weights_biases

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