# Jerry Chen and Instabase CEO Anant Bhardwaj | Building a System of Intelligence

Page: https://stenobird.com/podcast/greymatter/jerry-chen-and-instabase-ceo-anant-bhardwaj-building-a-system-of-intelligence
Text version: https://stenobird.com/podcast/greymatter/jerry-chen-and-instabase-ceo-anant-bhardwaj-building-a-system-of-intelligence.md
Podcast: [Greymatter](https://stenobird.com/podcast/greymatter)
Published: 2023-09-12T17:10:08+00:00
Episode link: https://pdst.fm/e/traffic.megaphone.fm/GRL9851776932.mp3?updated=1694538914
Audio file: https://pdst.fm/e/traffic.megaphone.fm/GRL9851776932.mp3?updated=1694538914
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JSON: https://stenobird.com/v1/public/podcasts/greymatter/episodes/jerry-chen-and-instabase-ceo-anant-bhardwaj-building-a-system-of-intelligence
Duration seconds: 2983

## Resource

Instabase CEO Anant Bhardwaj explains how to build a defensible 'system of intelligence' by layering custom workflows and layout-aware models on top of commoditized LLMs. The discussion explores moving beyond simple text processing to handle complex, structured enterprise data.

## Highlights
- Main idea: True enterprise value lies in a 'system of intelligence' that integrates LLMs with custom business logic and layout awareness
- Technical insight: Standard LLMs struggle with document intelligence because they lack the 2D spatial context (X and Y coordinates) essential for parsing forms
- Failure mode: Relying solely on foundation models creates a lack of moat; defensibility comes from handling the complex engineering of data extraction and classification
- Practical takeaway: Successful AI products must move from simple text prediction to understanding the structural relationship between data points in a document
- Leadership lesson: CEOs must maintain a 'learner's mindset' and stay deeply involved in technical details to avoid being blindsided by strategic errors

## Topics

System of Intelligence, Enterprise AI, Large Language Models, Document AI, Layout-aware Modeling, Machine Learning Infrastructure, AI Strategy, Foundational Models

## Chapters
- 1:10 — The Layers of AI Value Creation: An analysis of where value is accruing in the AI stack, from GPUs and cloud providers to foundation models and application layers.
- 5:10 — Evolution from Big Data to AI: Reflecting on the shift from the 'bring data to compute' era of big data to the current era of intelligent processing.
- 8:40 — The Complexity of Document Intelligence: The challenge of combining OCR, classification, and extraction into a single, high-speed business application.
- 16:10 — Building Defensible Workflows: Why user experience and custom user-defined functions (UDFs) are necessary to solve problems that raw models cannot.
- 23:30 — The Future of Neural Efficiency: Speculating on the potential for more cost-effective AI through optimized neural network activation.
- 27:20 — Understanding Model Weights: A technical look at how fine-tuning alters the floating-point numbers within a model to specialize in specific datasets.
- 45:50 — Lessons in Leadership and Resilience: Advice on staying curious, maintaining technical depth, and managing the inevitable mistakes of scaling a company.

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## Transcript

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