Episode 49: Why Data And AI Still Break At Scale (and What To Do About It) Vanishing Gradients podcast

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Episode 49: Why Data and AI Still Break at Scale (and What to Do About It)

3d ago 1:21:45

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If we want AI systems that actually work in production, we need better infrastructure—not just better models.

In this episode, Hugo talks with Akshay Agrawal (Marimo, ex-Google Brain, Netflix, Stanford) about why data and AI pipelines still break down at scale, and how we can fix the fundamentals: reproducibility, composability, and reliable execution.

They discuss:
🔁 Why reactive execution matters—and how current tools fall short
🛠️ The design goals behind Marimo, a new kind of Python notebook
⚙️ The hidden costs of traditional workflows (and what breaks at scale)
📦 What it takes to build modular, maintainable AI apps
🧪 Why debugging LLM systems is so hard—and what better tooling looks like
🌍 What we can learn from decades of tools built for and by data practitioners

Toward the end of the episode, Hugo and Akshay walk through two live demos: Hugo shares how he’s been using Marimo to prototype an app that extracts structured data from world leader bios, and Akshay shows how Marimo handles agentic workflows with memory and tool use—built entirely in a notebook.

This episode is about tools, but it’s also about culture. If you’ve ever hit a wall with your current stack—or felt like your tools were working against you—this one’s for you.

LINKS

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49 episodes

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