Gradient Dissent: Conversations on AI

Is video AI a viable path toward AGI?

Runway ML founder Cristóbal Valenzuela joins Lukas Biewald just after Gen 4.5 reached the #1 position on the Video Arena Leaderboard, according to community voting on Artificial Analysis.

Lukas examines how a focused research team at Runway outpaced much larger organizations like Google and Meta in one of the most compute-intensive areas of machine learning.

Cristóbal breaks down the architecture behind Gen 4.5 and explains the role of “taste” in model development. He details the engineering improvements in motion and camera control that solve long-standing issues like the restrictive “tripod look,” and shares why video models are starting to function as simulation engines with applications beyond media generation.

Connect with us here:

• Cristóbal Valenzuela: https://www.linkedin.com/in/cvalenzuelab
• Runway: https://www.linkedin.com/company/runwayml/
• Lukas Biewald: https://www.linkedin.com/in/lbiewald/
• Weights & Biases: https://www.linkedin.com/company/wandb/

In this episode of Gradient Dissent, Lukas Biewald talks with Tuhin Srivastava, CEO and founder of Baseten, one of the fastest-growing companies in the AI inference ecosystem. Tuhin shares the real story behind Baseten’s rise and how the market finally aligned with the infrastructure they’d spent years building.

They get into the core challenges of modern inference, including why dedicated deployments matter, how runtime and infrastructure bottlenecks stack up, and what makes serving large models fundamentally different from smaller ones.

Tuhin also explains how vLLM, TensorRT-LLM, and SGLang differ in practice, what it takes to tune workloads for new chips like the B200, and why reliability becomes harder as systems scale.

The conversation dives into company-building, from killing product lines to avoiding premature scaling while navigating a market that shifts every few weeks.

Connect with us here:

Tuhin Srivastva: https://www.linkedin.com/in/tuhin-srivastava/

Lukas Biewald: https://www.linkedin.com/in/lbiewald/

Weights & Biases: https://www.linkedin.com/company/wandb/

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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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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In this episode of Gradient Dissent, Lukas Biewald talks with Martin Shkreli — the infamous "pharma bro" turned founder — about his path from hedge fund manager and pharma CEO to convicted felon and now software entrepreneur. Shkreli shares his side of the drug pricing controversy, reflects on his prison experience, and explains how he rebuilt his life and business after being "canceled."

They dive deep into AI and drug discovery, where Shkreli delivers a strong critique of mainstream approaches. He also talks about his latest venture in finance software, building Godel Terminal “a Vim for traders", and why he thinks the AI hype cycle is just beginning. It's a wide-ranging and candid conversation with one of the most controversial figures in tech and biotech.

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In this episode of Gradient Dissent, host Lukas Biewald talks with Sualeh Asif, the CPO and co-founder of Cursor, one of the fastest-growing and most loved AI-powered coding platforms. Sualeh shares the story behind Cursor’s creation, the technical and design decisions that set it apart, and how AI models are changing the way we build software. They dive deep into infrastructure challenges, the importance of speed and user experience, and how emerging trends in agents and reasoning models are reshaping the developer workflow.

Sualeh also discusses scaling AI inference to support hundreds of millions of requests per day, building trust through product quality, and his vision for how programming will evolve in the next few years.

⏳Timestamps:

00:00 How Cursor got started and why it took off

04:50 Switching from Vim to VS Code and the rise of CoPilot

08:10 Why Cursor won among competitors: product philosophy and execution

10:30 How user data and feedback loops drive Cursor’s improvements

12:20 Iterating on AI agents: what made Cursor hold back and wait

13:30 Competitive coding background: advantage or challenge?

16:30 Making coding fun again: latency, flow, and model choices

19:10 Building Cursor’s infrastructure: from GPUs to indexing billions of files

26:00 How Cursor prioritizes compute allocation for indexing

30:00 Running massive ML infrastructure: surprises and scaling lessons

34:50 Why Cursor chose DeepSeek models early

36:00 Where AI agents are heading next

40:07 Debugging and evaluating complex AI agents

42:00 How coding workflows will change over the next 2–3 years

46:20 Dream future projects: AI for reading codebases and papers

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In this episode of Gradient Dissent, host Lukas Biewald talks with Christopher Ahlberg, CEO of Recorded Future, a pioneering cybersecurity company leveraging AI to provide intelligence insights. Christopher shares his fascinating journey from founding data visualization startup Spotfire to building Recorded Future into an industry leader, eventually leading to its acquisition by Mastercard.

They dive into gripping stories of cyber espionage, including how Recorded Future intercepted a hacker selling access to the U.S. Electoral Assistance Commission. Christopher also explains why the criminal underworld has shifted to platforms like Telegram, how AI is transforming both cyber threats and defenses, and the real-world implications of becoming an "undesirable enemy" of the Russian state.

This episode offers unique insights into cybersecurity, AI-driven intelligence, entrepreneurship lessons from a two-time founder, and what happens when geopolitical tensions intersect with cutting-edge technology. A must-listen for anyone interested in cybersecurity, artificial intelligence, or the complex dynamics shaping global security.

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In this episode of Gradient Dissent, host Lukas Biewald speaks with Captain Jon Haase, United States Navy about real-world applications of AI and autonomy in defense. From underwater mine detection with autonomous vehicles to the ethics of lethal AI systems, this conversation dives into how the U.S. military is integrating AI into mission-critical operations — and why humans will always be at the center of warfighting.

They explore the challenges of underwater autonomy, multi-agent collaboration, cybersecurity, and the growing role of large language models like Gemini and Claude in the defense space.

Essential listening for anyone curious about military AI, defense tech, and the future of autonomous systems.

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